{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Source -  https://www.kaggle.com/janiobachmann/bank-marketing-dataset. \n",
    "* The original dataset was sourced from UCI Machine Learning Repository and was contributed by [Moro et al., 2014]. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Listing 5-1  - Import required libraries\n",
    "\n",
    "\n",
    "#Import required libraries\n",
    "import torch.nn as nn\n",
    "import torch as tch\n",
    "import numpy as np, pandas as pd\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.metrics import  precision_score, recall_score,roc_curve, auc, roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DF Shape: (11162, 17)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2343</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>45</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2476</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         job  marital  education default  balance housing loan  contact  \\\n",
       "0   59      admin.  married  secondary      no     2343     yes   no  unknown   \n",
       "1   56      admin.  married  secondary      no       45      no   no  unknown   \n",
       "2   41  technician  married  secondary      no     1270     yes   no  unknown   \n",
       "3   55    services  married  secondary      no     2476     yes   no  unknown   \n",
       "4   54      admin.  married   tertiary      no      184      no   no  unknown   \n",
       "\n",
       "   day month  duration  campaign  pdays  previous poutcome deposit  \n",
       "0    5   may      1042         1     -1         0  unknown     yes  \n",
       "1    5   may      1467         1     -1         0  unknown     yes  \n",
       "2    5   may      1389         1     -1         0  unknown     yes  \n",
       "3    5   may       579         1     -1         0  unknown     yes  \n",
       "4    5   may       673         2     -1         0  unknown     yes  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listing 5-2  - Load data into memory\n",
    "\n",
    "\n",
    "#Load data into memory using pandas\n",
    "df = pd.read_csv(\"Data/bank.csv\")\n",
    "print(\"DF Shape:\",df.shape)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of Target Values in Dataset -\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "no     5873\n",
       "yes    5289\n",
       "Name: deposit, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listing 5-3  - Distribution of target values\n",
    "\n",
    "\n",
    "print(\"Distribution of Target Values in Dataset -\")\n",
    "df.deposit.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "deposit      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listing 5-4  - Distribution of na (null) values in dataset\n",
    "\n",
    "#Check if we have 'na' values within the dataset\n",
    "df.isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object    10\n",
       "int64      7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listing 5-5  - Distribution of distinct datatypes\n",
    "\n",
    "#Check the distinct datatypes within the dataset\n",
    "df.dtypes.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical cols: ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'poutcome', 'deposit']\n"
     ]
    },
    {
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       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
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       "      <th>poutcome</th>\n",
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       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>1270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age         job  marital  education  default  balance  housing  loan  \\\n",
       "0   59      admin.  married  secondary        0     2343        1     0   \n",
       "1   56      admin.  married  secondary        0       45        0     0   \n",
       "2   41  technician  married  secondary        0     1270        1     0   \n",
       "3   55    services  married  secondary        0     2476        1     0   \n",
       "4   54      admin.  married   tertiary        0      184        0     0   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome  deposit  \n",
       "0  unknown    5   may      1042         1     -1         0  unknown        1  \n",
       "1  unknown    5   may      1467         1     -1         0  unknown        1  \n",
       "2  unknown    5   may      1389         1     -1         0  unknown        1  \n",
       "3  unknown    5   may       579         1     -1         0  unknown        1  \n",
       "4  unknown    5   may       673         2     -1         0  unknown        1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Listing 5-6  - Extract categorical columns from dataset\n",
    "\n",
    "\n",
    "#Extract categorical columns from dataset\n",
    "categorical_columns = df.select_dtypes(include=\"object\").columns\n",
    "print(\"Categorical cols:\",list(categorical_columns))\n",
    "\n",
    "#For each categorical column if values in (Yes/No) convert into a 1/0 Flag\n",
    "for col in categorical_columns:\n",
    "    if df[col].nunique() == 2:\n",
    "        df[col] = np.where(df[col]==\"yes\",1,0)\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new_df.shape: (11162, 49)\n"
     ]
    },
    {
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       "      <th>default</th>\n",
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       "      <th>job_admin.</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>poutcome_failure</th>\n",
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       "      <th>day</th>\n",
       "      <th>education_secondary</th>\n",
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       "      <th>month_mar</th>\n",
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       "      <th>month_dec</th>\n",
       "      <th>job_technician</th>\n",
       "      <th>marital_married</th>\n",
       "      <th>job_unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1389</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>579</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>673</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   default  duration  job_admin.  job_blue-collar  poutcome_failure  \\\n",
       "0        0      1042           1                0                 0   \n",
       "1        0      1467           1                0                 0   \n",
       "2        0      1389           0                0                 0   \n",
       "3        0       579           0                0                 0   \n",
       "4        0       673           1                0                 0   \n",
       "\n",
       "   job_housemaid  month_jun  education_primary  day  education_secondary  ...  \\\n",
       "0              0          0                  0    5                    1  ...   \n",
       "1              0          0                  0    5                    1  ...   \n",
       "2              0          0                  0    5                    1  ...   \n",
       "3              0          0                  0    5                    1  ...   \n",
       "4              0          0                  0    5                    0  ...   \n",
       "\n",
       "   housing  education_unknown  campaign  previous  month_mar  poutcome_other  \\\n",
       "0        1                  0         1         0          0               0   \n",
       "1        0                  0         1         0          0               0   \n",
       "2        1                  0         1         0          0               0   \n",
       "3        1                  0         1         0          0               0   \n",
       "4        0                  0         2         0          0               0   \n",
       "\n",
       "   month_dec  job_technician  marital_married  job_unemployed  \n",
       "0          0               0                1               0  \n",
       "1          0               0                1               0  \n",
       "2          0               1                1               0  \n",
       "3          0               0                1               0  \n",
       "4          0               0                1               0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listing 5-7  - Onehot encoding for remaining non-binary categorical variables\n",
    "\n",
    "\n",
    "#For the remaining cateogrical variables; \n",
    "#create one-hot encoded version of the dataset\n",
    "new_df = pd.get_dummies(df)\n",
    "\n",
    "#Define target and predictors for the model\n",
    "target = \"deposit\"\n",
    "predictors = set(new_df.columns) - set([target])\n",
    "print(\"new_df.shape:\",new_df.shape)\n",
    "new_df[predictors].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape: torch.Size([8929, 48])\n",
      "x_test.shape: torch.Size([2233, 48])\n",
      "Y_train.shape: torch.Size([8929, 1])\n",
      "y_test.shape: torch.Size([2233, 1])\n"
     ]
    }
   ],
   "source": [
    "#Listing 5-8  - Prepare data for training and validation\n",
    "\n",
    "\n",
    "#Convert all datatypes within pandas dataframe to Float32 \n",
    "#(Compatibility with PyTorch tensors)\n",
    "new_df = new_df.astype(np.float32)\n",
    "\n",
    "#Split dataset into Train/Test [80:20]\n",
    "X_train,x_test, Y_train,y_test = train_test_split(new_df[predictors],new_df[target],test_size= 0.2)\n",
    "\n",
    "#Convert Pandas dataframe, first to numpy and then to Torch Tensors\n",
    "X_train = tch.from_numpy(X_train.values)\n",
    "x_test  = tch.from_numpy(x_test.values)\n",
    "Y_train = tch.from_numpy(Y_train.values).reshape(-1,1)\n",
    "y_test  = tch.from_numpy(y_test.values).reshape(-1,1)\n",
    "\n",
    "#Print the dataset size to verify\n",
    "print(\"X_train.shape:\",X_train.shape)\n",
    "print(\"x_test.shape:\",x_test.shape)\n",
    "print(\"Y_train.shape:\",Y_train.shape)\n",
    "print(\"y_test.shape:\",y_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Listing 5-9  - Define function to train model\n",
    "\n",
    "\n",
    "#Define function to train the network\n",
    "def train_network(model,optimizer,loss_function,num_epochs,batch_size\n",
    ",X_train,Y_train,lambda_L1=0.0):\n",
    "    loss_across_epochs = []\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        train_loss= 0.0\n",
    "\n",
    "        #Explicitly start model training\n",
    "        model.train()\n",
    "\n",
    "        for i in range(0,X_train.shape[0],batch_size):\n",
    "\n",
    "            #Extract train batch from X and Y\n",
    "            input_data = X_train[i:min(X_train.shape[0],i+batch_size)]\n",
    "            labels = Y_train[i:min(X_train.shape[0],i+batch_size)]\n",
    "\n",
    "            #set the gradients to zero before starting to do backpropragation \n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            #Forward pass\n",
    "            output_data  = model(input_data)\n",
    "\n",
    "            #Caculate loss\n",
    "            loss = loss_function(output_data, labels)\n",
    "            L1_loss = 0\n",
    "            \n",
    "            #Compute L1 penalty to be added with loss \n",
    "            for p in model.parameters():\n",
    "                L1_loss = L1_loss + p.abs().sum()                \n",
    "\n",
    "            #Add L1 penalty to loss\n",
    "            loss = loss + lambda_L1 * L1_loss\n",
    "\n",
    "            #Backpropogate\n",
    "            loss.backward()\n",
    "\n",
    "            #Update weights\n",
    "            optimizer.step()\n",
    "\n",
    "            train_loss += loss.item() * input_data.size(0)\n",
    "\n",
    "        loss_across_epochs.append(train_loss/X_train.size(0))\n",
    "        if epoch%500 == 0:\n",
    "            print(\"Epoch: {} - Loss:{:.4f}\".format(epoch,train_loss/X_train.size(0) ))    \n",
    "        \n",
    "    return(loss_across_epochs)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Listing 5-10  - Define function to evaluate model\n",
    "\n",
    "#Define function for evaluating NN\n",
    "def evaluate_model(model,x_test,y_test,X_train,Y_train,loss_list):\n",
    "\n",
    "    model.eval() #Explicitly set to evaluate mode\n",
    "\n",
    "    #Predict on Train and Validation Datasets\n",
    "    y_test_prob = model(x_test)\n",
    "    y_test_pred =np.where(y_test_prob>0.5,1,0)\n",
    "    Y_train_prob = model(X_train)\n",
    "    Y_train_pred =np.where(Y_train_prob>0.5,1,0)\n",
    "\n",
    "    #Compute Training and Validation Metrics\n",
    "    print(\"\\n Model Performance -\")\n",
    "    print(\"Training Accuracy-\",round(accuracy_score(Y_train,Y_train_pred),3))\n",
    "    print(\"Training Precision-\",round(precision_score(Y_train,Y_train_pred),3))\n",
    "    print(\"Training Recall-\",round(recall_score(Y_train,Y_train_pred),3))\n",
    "    print(\"Training ROCAUC\", round(roc_auc_score(Y_train\n",
    ",Y_train_prob.detach().numpy()),3))\n",
    "\n",
    "    print(\"Validation Accuracy-\",round(accuracy_score(y_test,y_test_pred),3))\n",
    "    print(\"Validation Precision-\",round(precision_score(y_test,y_test_pred),3))\n",
    "    print(\"Validation Recall-\",round(recall_score(y_test,y_test_pred),3))\n",
    "    print(\"Validation ROCAUC\", round(roc_auc_score(y_test\n",
    ",y_test_prob.detach().numpy()),3))    \n",
    "    print(\"\\n\")\n",
    "    \n",
    "    #Plot the Loss curve and ROC Curve\n",
    "    plt.figure(figsize=(20,5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(loss_list)\n",
    "    plt.title('Loss across epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "\n",
    "    #Validation\n",
    "    fpr_v, tpr_v, _ = roc_curve(y_test, y_test_prob.detach().numpy())\n",
    "    roc_auc_v = auc(fpr_v, tpr_v)\n",
    "\n",
    "    #Training\n",
    "    fpr_t, tpr_t, _ = roc_curve(Y_train, Y_train_prob.detach().numpy())\n",
    "    roc_auc_t = auc(fpr_t, tpr_t)    \n",
    "\n",
    "    plt.title('Receiver Operating Characteristic:Validation')\n",
    "    plt.plot(fpr_v, tpr_v, 'b', label = 'Validation AUC = %0.2f' % roc_auc_v)\n",
    "    plt.plot(fpr_t, tpr_t, 'r', label = 'Training AUC = %0.2f' % roc_auc_t)    \n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    \n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 - Loss:1.3867\n",
      "\n",
      " Model Performance -\n",
      "Training Accuracy- 0.944\n",
      "Training Precision- 0.926\n",
      "Training Recall- 0.957\n",
      "Training ROCAUC 0.987\n",
      "Validation Accuracy- 0.815\n",
      "Validation Precision- 0.796\n",
      "Validation Recall- 0.817\n",
      "Validation ROCAUC 0.876\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Listing 5-11  - Define Neural Network\n",
    "\n",
    "\n",
    "#Define Neural Network\n",
    "\n",
    "class NeuralNetwork(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        tch.manual_seed(2020)\n",
    "        self.fc1 = nn.Linear(48, 96) \n",
    "        self.fc2 = nn.Linear(96, 192)\n",
    "        self.fc3 = nn.Linear(192, 384)\n",
    "        self.out = nn.Linear(384, 1)        \n",
    "        self.relu = nn.ReLU()        \n",
    "        self.final = nn.Sigmoid()\n",
    "\n",
    "        \n",
    "    def forward(self, x):\n",
    "        op = self.fc1(x)\n",
    "        op = self.relu(op)        \n",
    "        op = self.fc2(op)\n",
    "        op = self.relu(op)\n",
    "        op = self.fc3(op)\n",
    "        op = self.relu(op)\n",
    "        op = self.out(op)\n",
    "        y = self.final(op)\n",
    "        return y\n",
    "    \n",
    "#Define training variables\n",
    "num_epochs = 500\n",
    "batch_size= 128\n",
    "loss_function = nn.BCELoss()  #Binary Crosss Entropy Loss\n",
    "\n",
    "#Hyperparameters\n",
    "weight_decay=0.0 #set to 0; no L2 Regularizer; passed into the Optimizer\n",
    "lambda_L1=0.0    #Set to 0; no L1 reg; manually added in loss (train_network)\n",
    "\n",
    "#Create a model instance\n",
    "model = NeuralNetwork()\n",
    "\n",
    "#Define optimizer\n",
    "adam_optimizer = tch.optim.Adam(model.parameters(), lr= 0.001,weight_decay=weight_decay)\n",
    "\n",
    "#Train model\n",
    "adam_loss = train_network(model,adam_optimizer,loss_function\n",
    ",num_epochs,batch_size,X_train,Y_train,lambda_L1=0.0)\n",
    "\n",
    "#Evaluate model\n",
    "evaluate_model(model,x_test,y_test,X_train,Y_train,adam_loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 - Loss:2.3865\n",
      "\n",
      " Model Performance -\n",
      "Training Accuracy- 0.862\n",
      "Training Precision- 0.821\n",
      "Training Recall- 0.907\n",
      "Training ROCAUC 0.937\n",
      "Validation Accuracy- 0.821\n",
      "Validation Precision- 0.784\n",
      "Validation Recall- 0.86\n",
      "Validation ROCAUC 0.896\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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XD2fFUkIkf3oS5wmZxFTrajyfkURdxj8Q50UTicLmO7j711m7TkvinEK0VDnZ3b+s6jWoxI3Aqkm87xLd8bINIlp2/WhmN+X6XJLnM5NouXUtcR7XnThfWVLJ/uOJBFsX4DMzm0ucI44i6nhV5Wyiy+N84hzy0eXvzovE8/2KeK0Xs2wXuBuI1vQvEedDdxOvFUQtofstutId4u6jiBpJtxDvzTgqHqGwMv2I57yAeM0Pc/ef3H0RUZj9reSxfpd9UNLtbDfinHYaUbNpJ4iWXMn9lXc/8dl/oNz6qj4LFcp6n68h3uduwFtZu1R6Pp64mrh4OscqHpH6cOIzMQV4Crg4+Q0hUm0W3T9FRCSbmXUhTmCbLKcGlYiIiNRzZtYX+Je7L6+bWkGyGOFvMtDf3V9LOx4RaXjU0klERERERKSeMLM9zGz1pIxCpsbSuymHJSINlJJOIiIiIg2Emd1jZj+Y2ZhKtpuZ3WRm48zsEzPbsrZjFJGVti0xutpMogvY/u7+U7ohiUhDpe51IiIiIg2EmfUhiss+4O6/rWD7XkTtvL2IGneD3L3SArkiIiIiy6OWTiIiIiINhLu/QQyyUJn9iISUu/u7wOpmtrziziIiIiKVUtJJRERERDI6sOxIUpOTdSIiIiLV1jjtAGpS27ZtvUuXLmmHISIiInnywQcfzHT3dmnH0dCZ2YnAiQDNmzffauONN045IhGpMT//DO5xK04G8M0sAyxaBCUlYFa2bvFiaNSobL+ffoLGjWHJklg2K7v/7PIu2Y/RkGS/HpnX0R2aNq14n4qOgXjtGjeO134FuUNpNSrulJYs+xaWeqwrHy5AcckKhyUFoIhSmrGERpRQTCNGU7JC52D1KunUpUsXRo0alXYYIiIikidmNjHtGOq574GOWcvrJeuW4e5DgCEAvXr1cp1/idSCpUth2rSYLy6Gt9+OdcXFkQQqLobPP4c2baC0NNaVlMBXX0Hr1rEus++XX0ZCyAzmzoUZMyLhsXRp9eMqKor7BujUKZIga64Js2fD1lvD9Omw+eaRGCkq+vXNDGbOhI03jvnyt8w+Fd3mzYP11698e1W3JUugS5d47qusAi1blm2DZffNXl7etor2bdYMmjf/5SX76Sf4+uvKX9Jp0+CTT+KlXFkffgizZi17X599BuPHx1NesmTlHwNgs81gtdWWXVdSAquuCjvsUDOPkYslS6Bfv9p7vELSvn38Ga200tL4u/viC2y3XfDz/gonnkSjVVdZoXOwepV0EhEREZGVMhwYaGZDiULic919asoxidRNP/0UyZxJkyLZ8/PP8O23keBYvDiSQWusUZYcKimJH3tvvx1Jm0yCaP58ePPN6j1206bxozHTAmbhQujWLZYbNYoMxIwZsNNOkXlYsCAes3XriK1nz9hn6VJYZ53IHGSOM4N114XOncsSSXXUd9/F0/3FSjS6GjUqcmBTpsD770f+qjx3eOKJFX+MldGzZ9l8ixaw1lpw0EHx9rdqFfnBXHXrBh2zLk80aVIzCTJJ2fffw6WXxgd56FDosQlMnIg1abJSd6uPhoiIiEgDYWaPAH2BtmY2GbgYaALg7rcDzxMj140DFgHHpBOpSAEpLYWxY+P21VeRRGrePJJC//tfJInatIlkTKbF0bx51X+cTEIo09qoZ8+ydVttFdM+fWCTTWK7O2y7bSSKMl2smjWLjEItmTkz8lnV9cMPMHo0TJ4Mn3766xYy2fc/YgS0bbtSPcgqNGVKzd5fee3bx1uTzR022CA+LjvssPwWQGutBb/91RijK6Z580gMiVRozhz4+99h0KD4/3XKKfG/rFGjGvngKOkkIiIi0kC4++FVbHfgtFoKRyQd8+fDRx9Fwmj27PhhNXp09EFq1Cj6NbVqFT+6Zs2qvMtZ8+ZJQZzSyC7svvuyLYnmzo1uZR07xv1lWgmts040L1lttTgu082sACxdCi+/HL9BX3214jo9EI227r+/5h533XWjMVV5mZd2002jF1xNmz0b9t+/ZhJapaWwxRbRYKx584pbOokUnNdegwMPjD/6I46Ayy6Drl1r9CGUdBIRERERkfpn3rwoXPPee9HfCeDuuyvet1GjSDK1aQPbbRfZiM02i6v8ixdHf6LttouMwvrrL1Ofpy5auDASR598AqedFrXBmzaNUk/ltW//63UlJdEzsEUL+MtfYPXVqx/DGmtEkqZ16xU7XkRWUHFx1Frr0CEyujvvDBddFEnyPFDSSURERERE6i53+Pjj6Ob2n/9EMe1vv614386dYeLEuJrfty9stBGsvXathltTPvmkrLXRW29FIimXujpfflmujlHiD3+AHj2ixdGpp0Z+rWtX1eoRqTfc4emn4YILoqXle+9F39U8FxrTvxARERERESlspaWRWXn3Xfjgg7hKX1oa3eKWLFk2i9K4cSST9twzWih17w69e0e9o5ouDFTDpkyJnNkLL0QPvIpMnhy/FTOtklq0iG5xS5fCvvtW/RidOsXL94c/ROumjTduuKN9iTQYb7wB554b/0N/8xs4//zK+8/WMCWdREREREQkXd99B1OnwjffxKhvkyfDmDGxbtSoipvmtGoFvXpF0mnLLeGEEyKDUuAVk90jV7ZgwbLr990Xfvxx2XUVtTIqTkZY++1voX9/OO+8/MQpIvXE00/DAQdEd7o774Sjj67VJoxKOomIiIiISP5Nnw5ffBEVqsePh4cfji4eixYt/7h1140ucHvvHZmZ7t1rdYS2FTVu3K+TSBdcEKOxLc/gwTFY3VZbqWubiKygCRPi1rdvtPq8+WY47riKK/bnmf6NiYiIiIhIzZk6FT77LFogjR0LM2bANdf8er9NN40ubzvvHFWtf/vbqFrduXO0YlprrYLPuixZAjfdVNa46pVXYiC6KVOigVZl+vWDgQOX7ULXpEnUKi/whloiUshmzIArr4Rbb43/pWPHxj+agQNTC6mw/4sXgAVLihn+8RS26dqGDdoV/hUVEREREZFaNWtWDGE2fnxkWpYu/fU+668fQ5XttBNsvTX07Fl50aICNWVKdIkrLY3k0pgxcPvtFe+7+eZRTurUU2OaUVQEffrU+cHvRKTQLFgAN9wA118fowoceyxcfHH800mZkk5VmLNoKec/9SnXHriZkk4iIiIiIrNnw5Ah8Oab8Pzzy27bb78oUtuxYxTvbtoUunSB1VdPJdTqKi6OhgHvvFNWc3zJEjjllMqP6ds3Bn/K7N+qVUH8zhORhuS11yLJ9Ic/REunjTdOO6JfKOlUhaKkorvjKUciIiIiIlLLFiyAJ5+ModJuvrlsmLSMzp2jm9zRR8OBB6YWZk3ZdNOyUeHKa9UKrrsuykmZRYulNm1SKZEiIg1daSk8+mi0NB04EPbZBz7+OJpZFhglnaqQGUWwVDknEREREWkopk6FBx5Ydmi01VePJNT++8fV9P7961yTnh9+iFHDM93b/vvfKCfVqFHUOZ89O9Y//HDUV8po2jTqmYuIpModXnoJ/vpX+Oij+Ed16qnxv7gAE06gpFOVjKSlk5JOIiIiIlIfTZ8Or78O778fVaw//xz+/e+y7WecEcOurbVWejFWobQ0usFlmzUrBsobOTJGDJ8/HxYvLtu+5prRnW7uXDj44KhjXlwM114LG25Yu/GLiFRpzJj4f/zaa9Ft+cEH4YgjCj75r6RTFTItndS9TkRERETqvBkzYlSjYcMiCzNhwq/3ad48us2dfXYMtb3BBrUeZnUsWgQtW0biaXnat4dOnaKB1mmnlZ3ni4gUNPf4h1VcHCODDhoEJ51UZwZjUNKpCr8knZRzEhEREZG6qLgY3n4bhg6F224rW7/++nDCCZG12XxzOOiguHpeoNmYTK+SN94o+6118cXL7nPNNcsut2wJe+xR8HkzEZFfmzIFLr00/offfXeM+vndd3Um2ZShpFMVyrrXKeskIiIiInXIFVdEsuk//ylb16MHXHghHHpowSaXMiZOhPvui0RTcTG8917l+95wQ7Reatq01sITEcmPOXOin++NN8Y/v1NPLWvtVMcSTqCkU5XKuteJiIiIiBS4H3+E//f/4K67yta1bg3HHANnngkdO6YXWw6mTo1k06BB0TArW48e0Tjr8sths83K1hd4ORMRkdyNGBEXBX78Meo1XX45dO2adlQrRUmnKmSu/6ihk4iIiIgUrOJi2HffZVs1bbstPPsstGmTXlzV8M47y44YB9F97pxzykabExGpd4qLY+SDtdeOEQ369IFLLonudPWAkk5VKDJ1rxMRERGRAnbHHXDyyWXLt90Gxx8PjevGqf6kSVG3PHO6fcghcPTRsMsu6i4nIvWYe4wUev750LZtjCK67rox3GY9osaoVch0rytVzklERERECsHixXD11bDxxnGymkk4nXoqLF0ay3Ug4TR1KgwcGCPKZRJOL7wAjz4ag+Yp4SQi9dYbb8D228MBB8TQm3/+c9oR5U3hfxul7JdC4inHISIiIiIN3KRJ0L8/vPlmLLdsGf3R2rePJNSGG6YbXzX89BNssQVMnx7L++wDTz1VJ3JlIiIr59FH4bDD4n/3kCFRc68e//Orv8+shljSFkzd60REREQkFTNnwoABZfWamjWDvfaCYcPSjStHS5dCSQksWhSjzC1eHNOMBQtUs0lE6rkJE2DKlLhQ8Pvfwz//CSeeCKutlnZkeZe3pJOZdQQeANYmGgoNcfdB5fbpD5xL1OueD5zi7qOTbROSdSVAsbv3ylesy6NC4iIiIiKSihNOgFdfhW++ieVWreCmm+Coo9KNK0cLF0b3ufvu+/W2pk2jZu748dCkSa2HJiJSO2bOhCuvhFtvhW7d4NNPI9FUj7vTlZfPlk7FwFnu/qGZtQQ+MLMR7v551j7fAju6+49mticwBNgma/tO7j4zjzFWyTKFxNXBTkRERERqw6xZUVQ2Y8MN4bzz4Ljj0ospB8XF8P77cPvtMHp03DJOOw06dowegSecoESTiNRzCxZEa6brrosM/LHHxnCcmaLRDUjekk7uPhWYmszPN7MvgA7A51n7vJ11yLvAevmKZ0WppZOIiIiI1IopU2KI7BkzytYtXFgnul/cfXcMmJete3fo2hXuvXfZHJqISL33/PPwt79FofArr4RNNkk7otTUyuh1ZtYF2AL433J2Ow74T9ayAy+Z2QdmdmL+olu+IlMhcRERERHJs6FDoUOHsoTTkCFx1bOAE04LF8Irr0RDrEzC6Y9/jDrnP/8Mn30GzzyjhJOINAClpfDII5GBBzjoIPjgA3jyyQadcIJaKCRuZi2AYcCf3X1eJfvsRCSddshavYO7f29mawEjzOxLd3+jgmNPBE4E6NSpUx7ij2mpmjqJiIiISE378UfYZhv4+utYHjAgiiAVcBeM66+HK66AuXOXXX/HHVEXV0SkwXCHESOiC/RHH0GfPtGVrqgIttwy7egKQl5bOplZEyLh9JC7P1nJPpsBdwH7ufuszHp3/z6Z/gA8BfSu6Hh3H+Luvdy9V7t27Wr6KWQ9Tt7uWkREREQakuLiuBrepw+0aRMJp/XWgy++gPvvL8iE08KFMHJkdJk755xIOK29dsy/+WacKyvhJCINyqefwq67wh57wOzZ8OCD8NprBfk/PE35HL3OgLuBL9z9hkr26QQ8CRzp7l9lrW8OFCW1oJoDuwOX5SvW5dHnRURERERqzOTJUVE721/+AjdUeLqcuueei5pMw4Ytu/6DD3QRX0QaKPdIFCxYEImnG2+Ek0+GVVZJO7KClM/uddsDRwKfmtnHybrzgU4A7n478DdgTeDWZJS4YnfvBawNPJWsaww87O4v5DHWSv1S00lNnURERERkRZSUxIh0N90UBWUBWreGTz6JBFSBXeVcuBB69IAlS2DatLL1e+8Np54Ku+8OjfNepENEpMBMmQKXXgpNm8LNN8O228LEibDqqmlHVtDyOXrdSMoGf6tsn+OB4ytY/w2weZ5Cq5bMEyhVzklEREREquPZZ6Mg+IgRsHhx2frBgyN7U0AWL47fUwMGwFtvla0/4AA46yzYfvv0YhMRSdWcOXDttdGiqbgYBg4sa+2khFOVdI2iCvZLS6eUAxERERGRusEdrroKLrydzKs1AAAgAElEQVSwbN1550HPntCvX7RyKhDusP76cbE+2yGHwEMPqUWTiDRw//kP9O8fgz4ccQRcfjl07Zp2VHWKvkaqUJQ0dXKUdRIRERGRKixYAGutBT/9FMsvvwy77JJuTJVYtCjyX8XFsXzNNdChA/zxj+nGJSKSqpKSSDK1bQubbBJNPS+7DLbYIu3I6iQlnaqQaemk7nUiIiIislz//S/stFPM9+4Nr74KzZunGlJ5X38dv6V23HHZHn+TJsUAeiIiDZY7DB8O558PnTpFK6cuXeCZZ9KOrE4rSjuAOkP960RERESkIu+/H7U9MgmnjTaCd98tuITTo49GaNtsU5ZwuuYa+PlnJZxEpIEbORJ22AH23z+afx5/vHIANUQtnXJghjrXiYiIiMiyioth333janjGyJExolEBjUg3dSr06QPjxsXyRRfBdttFayfVwBWRBu/BB2MUhfbtY/CHY45RQbsapFcyB0VmSnKKiIiISJnFi6FbN5g8OZY/+wy6d083pgq4l43qDfDkkzEinYhIgzZxIsyYAb16wX77wXXXxaiiq62WdmT1jpJOOTCgVFknEREREcno378s4TR3LrRqlW48FViyJEb2ziScCjRMEZHaM3NmjC46eDBsuml0j27VCs4+O+3I6i3VdMqButeJiIiICABffRX90p58MkY1Ki0tyEzODTdAs2Zw112xPG1aQYYpIlI7Fi6EK66ADTaAQYPgyCPh6acLqit0faWkUw4Mda8TERERafBuvBF+8xt4443oo/b22wX3g6WkJMI766xYPvTQqGm+9trpxiUikqonnoiCdjvvDGPGREZeIyjUCnWvy0G0dFLWSURERKTB2mcfeO65mO/fH/71r3TjyfLKK/Fbyj0STBkXXBAX9kVEGpzSUnjsscjE9+8ft+7dYeut046swVHSKQdmGi1RREREpMG6//5IOBUVRT+1du3SjgiA22+PsiRjxsTy7rvDLrtA69Zw+unQt2+q4YmIpGPECDjvPPjww/in2L9/jEanhFMqlHTKQXSvU9ZJREREpMG57bYY0QiicHiBJJxmzYJTTon5Ll3g4IPh2mtTDUlEJF2ffAJnnhnNP7t0gQcfhCOOSDuqBk9JpxwUqaWTiIiISMOz117wn//E/ODBsO666caT5cgjY3rFFdGNTkSkwXKP7kkzZsDo0VEo/KSTYJVV0o5MUNIpJ2ZGqZJOIiIiIg3HtdeWJZzeegu22y7deBJLl8IDD5SFduKJ6cYjIpKaqVPh0kujT/Hf/x5d6SZOhNVWSzsyyaKkUw4MFRIXERERaTBefRXOPTfm586FVq3SjSexcCG0aFG2PHJkwfT2ExGpPXPmxIWBG2+En3+GM84o26aEU8FR0ikX6l4nIiIi0jCMGhVXywFuuqlgEk4An30W09at4emnYfvt041HRKTWPfssHHUUzJ4Nhx0Gl18OG26YdlSyHEo65aDILO0QRERERCTf3ONHDET/tX790o2nnG22iekjj2hkOhFpQEpKotVpmzaRYNpmmyhot+WWaUcmOShKO4C6wAxK1dRJRERE6jgz62dmY81snJmdV8H2Tmb2mpl9ZGafmNleacSZih9+gKIiGD8eDjig4BJOGS1awJ57ph2FiEgtcId//xs22wxOOCHWbbwxPP+8Ek51iJJOOTDUvU5ERETqNjNrBAwG9gS6A4ebWfdyu10IPObuWwCHAbfWbpQpcY8fNRkPP5xeLOWUlsINN0D35J0666x04xERqRUjR8IOO8D++0NxMRx+eNoRyQpS0ikHZqZC4iIiIlLX9QbGufs37r4UGArsV24fBzJFjFoDU2oxvvTccw9Mnw5bbRUJqGbN0o7oF+PGRaLpiy+iIdZeDaftmYg0VHfdBf/3f/Dtt3DHHTBmDBx0UNpRyQpSTaccFKmQuIiIiNR9HYBJWcuTgW3K7XMJ8JKZnQ40B3at6I7M7ETgRIBOnTrVeKC1aupUOP74mH/iiXRjyXLvvTB0KLz0Uiw/+igccki6MYmI5M3EiVG3abPNoovzrFlw+ukaja4eyFtLJzPrmNQE+NzMPjOzP1Wwj5nZTUldgU/MbMusbUeZ2dfJ7ah8xZkbo1RJJxEREan/Dgfuc/f1gL2AB83sV+eL7j7E3Xu5e6927drVepA1xh123DHmhw6FLl1SDSdj2jS49VZ4552ol7v55rDHHmlHJSKSBzNnwplnwkYbwamnxro114Rzz1XCqZ7IZ0unYuAsd//QzFoCH5jZCHf/PGufPYFuyW0b4DZgGzNrA1wM9CKaeX9gZsPd/cc8xlupGLxOWScRERGp074HOmYtr5esy3Yc0A/A3d8xs2ZAW+CHWomwNj3xBBx8cNnyoYemF0uW118vG5lujz3ghRdSDUdEJD8WLoR//hOuuw4WLICjj4ZLLkk7KsmDvLV0cvep7v5hMj8f+IJo1p1tP+ABD+8Cq5vZusAewAh3n50kmkaQnAClQd3rREREpB54H+hmZuubWVOiUPjwcvt8B+wCYGabAM2AGbUaZW0YP74s4bTWWtGtowBMn16WcDrgABgyJNVwRETy54EH4KKLYOed4dNP4e67oWPHqo+TOqdWCombWRdgC+B/5TZVVFugw3LWp8IwSpV1EhERkTrM3YuBgcCLxMXAx9z9MzO7zMz2TXY7CzjBzEYDjwBHu9fDk6Azzojps89GpqcA6lJdcQWss07Mn3EGPPlkQYQlIlIzSkujON2wYbF87LHw7rvw1FNlw3NKvZT3QuJm1gIYBvzZ3efl4f7zXsjS1NJJRERE6gF3fx54vty6v2XNfw5sX9tx1aqvv4bnn4d27QpiKLjnn4e99y5b7toVrr8+vXhERGrcyy/DeefBBx9Av35w4IGwyipRtE7qvby2dDKzJkTC6SF3f7KCXSqrLZBLzQGgdgpZGqroJCIiIlLnlZbC7rvH/F13ZQp3pmb27LKEU48eMHly9Pxr0iTVsEREasbo0bDbbnGbOTO61D37bNpRSS3L5+h1BtwNfOHuN1Sy23BgQDKK3e+Aue4+lWj2vbuZrWFmawC7J+tSYWZq6SQiIiJS1516KkyYAL17w777Vrl7Po0fX9bQat99YcwY6JBaMQkRkTz47jv46CO48UYYOxaOPBIaNUo7Kqll+exetz1wJPCpmX2crDsf6ATg7rcTzbv3AsYBi4Bjkm2zzexyouAlwGXuPjuPsS5XdK9T1klERESkznr+ebjjjph//fXUwnCHgQPh1lvL1t15Z2rhiIjUnKlT4bLLokDdxRfDPvvAt99Cy5ZpRyYpylvSyd1HEj3TlrePA6dVsu0e4J48hFZtZupeJyIiIlKnZUarGzkSmjVLLYyhQ8sSTpdcAiedFAPoiYjUWXPnwrXXRoumpUvhzDNjvZkSTpL/QuL1gWFq6SQiIiJSV40bB4sWwcYbw/bp1UkvLYUjjoj5kSNTDUVEpGY8/TQcd1wUqTv8cLj8cthgg7SjkgKS10Li9UWRWjqJiIiI1F19+8b0lltSC2HCBGjdOuY320wJJxGpw0pKonUTwPrrw9Zbx8h0Dz+shJP8ipJOOTAzSpV1EhEREal7Fi+G77+H1VeHXXZJJYQ33ojfZQsWxPJ//pNKGCIiK8cdhg+HzTeP4nQQ8y+8AFtumW5sUrCUdMqBoULiIiIiInXS/vvH9Oqra+0h3WH6dPjmGzjkENhxx1h/0EHRxa59+1oLRUSkZowcCf/3f7DfflG3ab/90o5I6gjVdMqBComLiIiI1EGffgovvhjzf/xj3h/OHUaNgt69f73t5pvLGgaIiNQpt94Kp50G664Lt98Oxx4LTZqkHZXUEUo65cBMhcRFRERE6pwBA2L68cfQokVeH2rUqChrku2226Bx4xg4L1PPSUSkTpg4ERYuhO7d4YADoobTGWdA8+ZpRyZ1jJJOOYjudWlHISIiIiI5mzUrkk2tWkXl7jy55Zb4HZZ9rjh8OOy9NxSpkIWI1DUzZ8JVV8HgwdGd7uWXo4XTX/+admRSR+mrMAdmSjqJiIiI1ClbbBHTwYPjZC4P3nwTTj89zhN79IARI6Jm0+9/r4STiNQxCxfCFVfE6HODBkH//nDPPWlHJfWAvg5zUGSGq6qTiIiISN0wdSpMmhTzhx+el4d4+mno0yfmb74ZxoyBXXfNW35LRCS/hgyBiy6CnXaCTz6JhFOnTmlHJfWAutflqFQ5JxEREZG64YorYvrEE9CoUY3drTv86U/w0kswdmysGzoUDj20xh5CRKR2lJbC449HjaZ99oGTT4ZttoHttks7Mqln1NIpB1FIPO0oRERERCQnL7wQ0732qtG7/dOfolXT2LGw9tpw7bVKOIlIHfTyyzHM5mGHwZ13xrpVV1XCSfJCLZ1yEK2klXUSERERKXilpfDNN1GPZNVVa+xuFy+OhBPAhAnQuXON3bWISO0YPRrOOScK0HXqBPffH/8rRfJILZ1yUFSk7nUiIiIiBW/hQlhrrZivoazQnDmw5ppl+avjj1fCSUTqqC+/hA8/hH/+E776CgYMqNEuyCIVUUunHBiGq3+diIiISGG75x6YNSvmTzutRu7yxhth9uyYP+YYuO22GrlbEZH8mzYNLrsMunaFs8+Ggw+GPfeEVq3SjkwaECWdcmCmznUiIiIiBW/p0pjOmAFt29bIXZaWlk01Mp2I1Alz58J110WLpqVLI+EE0YVHCSepZUo65UCFxEVEREQK3Pvvl/2wquEfVWZKOIlIHTFsGJx0UrT6POwwuPxy2HDDtKOSBkw1nXJgQKmyTiIiIiKF69FHY3rOOdC0abqxiIjUppISWLAg5jt0gK22gg8+gEceUcJJUqekUw50ZUtERESkgI0fD//4R3QdufbaGrnLcePg0kujkYCuPYpIQXKHZ56Bnj3hrLNi3e9+By++CFtumW5sIgl1r8uBoZMNERERkYJ11FEx/dvfVvqu3KPm7oQJZet23XWl71ZEpGa99Race25Mu3WD3XdPOyKRCqmlUw6KzHCVEhcREREpPLNnx48ugAsvXOm722ijsoTTQw/BokUwYsRK362ISM0ZNAh22AG++QZuvx0++wwOPDDtqEQqpJZOOTArG7lERERERArI44/H9P77oVGjFbqLBQvgqadgwICydXPmQOvWNRCfiEhN+O47WLIkWjXtvz8sXAh/+hM0b552ZCLLpZZOOTDU0klERESkIE2cGNMDDljhu3jssWUTTmPHKuEkIgVi1qyo17TRRnDmmbGuc2c4/3wlnKROyFvSyczuMbMfzGxMJdvPMbOPk9sYMysxszbJtglm9mmybVS+YsyZqaaTiIiISEG6+uqYtmixQofPnw9/+UvMf/JJnPNttFENxSYisqIWLoQrr4wiczfeCIcfDoMHpx2VSLXls6XTfUC/yja6+3Xu3tPdewJ/BV5399lZu+yUbO+VxxhzUqSkk4iIiEjhyZygde26QsMNl5TAXnvBvHmw2mpKNolIARk0KOrU9e0bGfF774VOndKOSqTa8lbTyd3fMLMuOe5+OPBIvmJZWdG9TkWdRERERArKuHExPeKIah86bRqsu27Z8rx5K1wSSkRk5ZWWwhNPQJs2MWTmwIGw446w/fZpRyayUlKv6WRmqxEtooZlrXbgJTP7wMxOrOL4E81slJmNmjFjRp5iVEsnERERkYLz2Wcx/d3vqnXYlCllCafu3eHtt5VwEpEUvfwy9O4Nhx4ao9EBtGqlhJPUC6knnYDfA2+V61q3g7tvCewJnGZmfSo72N2HuHsvd+/Vrl27vARYZKYy4iIiIiKFZPHisuLh3brlfNiwYdChQ9nyp5/CttvWcGwiIrn4+GPYfXfYbTeYMQPuuw8efTTtqERqVCEknQ6jXNc6d/8+mf4APAX0TiGuX5hBqZo6iYiISIEws6ZmtmHacaTqllti2qdPtYoxvfxyTM86K3qzFBXC2bCINEwffhi3G26IYTOPOkrNLqXeSfVr1sxaAzsC/85a19zMWmbmgd2BCkfAq03KOYmIiEghMLO9gU+BEclyTzN7Kt2oUnDDDTF94YVqHWYG7drB9devUO1xEZEVN20anHpq2Sh0AwbA+PExhGazZunGJpIneUs6mdkjwDvAb8xsspkdZ2Ynm9nJWbsdALzk7guz1q0NjDSz0cB7wHPuXr2ziRpm6l4nIiIiheMyYBtgDoC7fww0rFZPX34JU6fCnnvCqqtW69DnnoPi4jzFJSJSkblzYyS6DTaAO++M/18AjRtD69bpxiaSZ/kcve7wHPa5D7iv3LpvgM3zE9WKKTLU1ElEREQKxc/uPseWbabTsE5ULr00pueeW63D7roLvvsuD/GIiFTmsceiddOsWVEo/IorYMOGdZ1AGra8JZ3qEwNKG9apnIiIiBSuL8zsEKDIzNYHzgDeTTmm2lNSAkOHxnyfSseaqdAJJ8R03LgajklEJFtJCSxZAqutBm3bwhZbwDXXwFZbpR2ZSK1T6cQcRPc6ZZ1ERESkIAwEtgJKgSeBJcCfUo2oNg0bFtMzz6xWUab33otphw7Rw0VEpMa5w7PPQs+e0Z0OYOedYcQIJZykwVLSKQdFpt51IiIiUjD2cPdz3X2L5HYesGfaQdWaY46Jaf/+OR9SUhKj1QHcfHMeYhIRefvtaH35+9/D4sWw/fZpRyRSEJR0yompe52IiIgUigsrWHdBrUeRhgsugEWLYNttYcstcz7so49g5MiY1+9AEalx110X/1zGjYPbboPPP4cDD0w7KpGCoJpOOTADV1MnERERSZGZ7QH0AzqY2Q1Zm1oRXe3qt48/hquuivm77sr5sJIS2HrrmH/hBVhrrTzEJiINz6RJUFoKnTvDvvvCzz/Dn/4EzZunHZlIQVFLpxzkXi1AREREJG9+AMYAi4HPsm4v0RC6173wQkxfeQW6d8/5sMGDy+Z3262GYxKRhmfWLDj7bOjWDc45J9b95jdw/vlKOIlUQC2dclBkpppOIiIikip3/wj4yMwecvfFacdT6zJJp+22q9Zh06fH9McfoUiXW0VkRS1cCIMGwd//DgsWwIABcOmlaUclUvCUdMqBGZQq6yQiIiKFoYOZXQl0B5plVrr7RumFVAs+/xyaNIFmzareN0umR95qq+UhJhFpOP7+d7j88uhKd9VV0KNH2hGJ1Am63pMDM1DKSURERArEfcC9RAWAPYHHgEfTDCjvSkthxgzYf/+cD3n55bL6TT17QtOmeYpNROond3j8cXjzzVj+059iRIJ//1sJJ5FqUNIpB4apkLiIiIgUitXc/UUAdx/v7hdS32s6ZX70demS0+5LlkT9phkzYI01lq3rJCJSpVdegd694ZBDYjQ6gDXX1PCXIitASaccqKWTiIiIFJAlZlYEjDezk83s90DLXA40s35mNtbMxpnZeZXsc4iZfW5mn5nZwzUZ+Apxh759Y/7ww3M6ZHFS8eqww2D27GqXgRKRhuqjj2D33WHXXaMg3L33woMPph2VSJ2mmk45MBUSFxERkcLxF6A5cAZwJdAaOLaqg8ysETAY2A2YDLxvZsPd/fOsfboBfwW2d/cfzWytPMRfPddfH9MDDoAttqjWob175yEeEam/3noLPvgA/vEPOPXUateQE5FfU9IpBwbqXiciIiIFwd3/l8zOB44EMLMOORzaGxjn7t8kxwwF9gM+z9rnBGCwu/+YPNYPNRX3ChszJqaPPJJuHCJS/0ybFsXBe/WCY46BE0+EI4+E1q3Tjkyk3lD3uhwUqXudiIiIFAAz29rM9jeztslyDzN7APhfFYcCdAAmZS1PTtZl2wjYyMzeMrN3zaxfjQS+Mt54I4r2rrJKzofssUdMi3SmKyIVmTcPLroINtgA7rgDJkyI9U2bKuEkUsP0VZwDM6NULZ1EREQkRWZ2NfAQ0B94wcwuAV4DRhPJoprQGOgG9AUOB+40s9UriOVEMxtlZqNmzJhRQw9dgfnz48fg3LnVOmzs2JgeckjNhyQiddwjj0DXrnDFFbDPPvDFF3DppWlHJVJvqXtdDqJ7XdpRiIiISAO3H7C5u/9kZm2IVkubZrrL5eB7oGPW8nrJumyTgf+5+8/At2b2FZGEej97J3cfAgwB6NWrV/7Okp59Nqann57zIR9+CHPmwEknwbrr5ikuEalbSkrg55+jRlPLllEf7uqro1udiOSVWjrlwpR0EhERkdQtdvefANx9NvBVNRJOEImjbma2vpk1BQ4Dhpfb52milRNJF76NgOo8Rs368suYDhiQ8yGffBLTXXbJQzwiUre4R/K6Z8+o3QSw994wYoQSTiK1RC2dclBklnYIIiIiIl3N7Mlk3oD1s5Zx9z8s72B3LzazgcCLQCPgHnf/zMwuA0a5+/Bk2+5m9jlQApzj7rPy8WRy8u23UZhpnXVyPuSqq2KqketEGri334Zzz4WRI2HDDWGrrWK9ftuJ1ColnXJgoJpOIiIikrYDyy3fUt07cPfngefLrftb1rwDZya3dI0aBQ8+WK2E08yZ8PXXsOqq0LlzHmMTkcJ21VVwwQWw9tpw661w/PHQpEnaUYk0SEo65cDUvU5ERERS5u6vpB1Drdp665jefHPOh4wfH9Njj81DPCJS2CZNipaRHTpEgXB3+POfoXnztCMTadBU0ykHhuEo6yQiIiJSK15J8mvrrgsHHZTTIa++Wpaf2nvvPMUlIoVn9mw45xzo1g3OPz/WbbZZtHRSwkkkdXlLOpnZPWb2g5mNqWR7XzOba2YfJ7e/ZW3rZ2ZjzWycmZ2XrxhzVVSklk4iIiIiteaMM2L6xhs57V5aGoXDH3oIWreGLl3yF5qIFIhFi2IEuq5d4R//gMMOg8suSzsqESknny2d7gP6VbHPm+7eM7ldBmBmjYDBwJ5Ad+BwM+uexzhzYJQq6SQiIiIFxMxWSTuGvHjnHfj8c9hkkyj+m4Onn47pvvvCnDlxqIjUc5dcEi2b+vSJYSvvu0/F3EQKUN6STu7+BjB7BQ7tDYxz92/cfSkwFNivRoOrphjgQFknERERSZ+Z9TazT4Gvk+XNzSz3wkeF7pRTYvrwwzkfMn9+TDMj14lIPeQOTzwB770Xy2eeCW++CcOHw29/m25sIlKptGs6bWtmo83sP2bWI1nXAZiUtc/kZF1qilRIXERERArHTcA+wCwAdx8N7JRqRDVlyRIYPRp69YKePXM+LFPGReVbROqpV1+FbbaBgw+G226LdeusAzvskG5cIlKlNJNOHwKd3X1z4Gbg6RW5EzM70cxGmdmoGTNm1GiAvzwGRqmyTiIiIlIYitx9Yrl1JalEUtOeeiqmBxyQ8yFjxsCUKTGvnjUi9cxHH8Eee0TRtmnTogvdXXelHZWIVENqSSd3n+fuC5L554EmZtYW+B7omLXresm6yu5niLv3cvde7dq1y0usZupcJyIiIgVjkpn1BtzMGpnZn4Gv0g6qRjz+eEyPPTbnQzKtnB56KFMSQUTqjREjYNSoKBT+1Vdw1FHQqFHaUYlINaSWdDKzdczi1CA5cSoimom/D3Qzs/XNrClwGDA8rTgBDHWvExERkYJxCnAm0AmYDvwuWVe3vfEGPPkktGkT3WZy1KxZTA85JE9xiUjtmT4dBg6EoUNj+fTT4Ztvon5T5o9dROqUxvm6YzN7BOgLtDWzycDFQBMAd78dOAg4xcyKgZ+Aw9zdgWIzGwi8CDQC7nH3z/IVZy7MDFfWSURERApDsbsflnYQNe7442O6Al1nNtkEGuftrFZE8m7ePLj+erjhBli8GNZeO9avumrcRKTOytvXs7sfXsX2W4BbKtn2PPB8PuJaEaZC4iIiIlI43jezscCjwJPuPj/tgFbazJnw9ddRQLwa9ZxEpB546CH485/j/8Ahh8AVV0C3bmlHJSI1JO3R6+oEw1TTSURERAqCu28AXAFsBXxqZk+bWd1u+XTllTE97rh04xCR2lFSAkuXxnyTJjFa5ahR8OijSjiJ1DNKOuUgWjop7SQiIiKFwd3fdvczgC2BecBDKYe0coYn5TurmXT66aeoPf7zz3mISURqnjs89xxssUV0pwM4+OAoGL7VVunGJiJ5kVPSycw2MLNVkvm+ZnaGma2e39AKR5FGrxMREZECYWYtzKy/mT0DvAfMALZLOayVs3AhbL11tHiohjlzYrr55nmISURq1jvvwI47wj77wKJFUYwNNOykSD2Xa0unYUCJmW0IDAE6Ag/nLaoCY2aUqqWTiIiIFIYxxIh117r7hu5+lrv/L+2gVtjHH8eIVSvQyuHss2O6++41HJOI1KxLL4XttoOvvoLBg+GLL1S/TaSByLWQeKm7F5vZAcDN7n6zmX2Uz8AKiaFC4iIiIlIwurp7adpB1JjBg2O6337VPvTh5BLoPvvUYDwiUjMmTYKmTWMkuj33hEaNomB4ixZpRyYitSjXpNPPZnY4cBTw+2Rd9do/12FmKiQuIiIi6TKzf7j7WcAwM/vVqYm7/yGFsFbe0qUxJHq/ftU+tGlTOOEEaN8+D3GJyIqZPRuuvhpuvhmOPhpuvx16946biDQ4uSadjgFOBq5092/NbH3gwfyFVVhUSFxEREQKwKPJ9JZUo6hpP/0EnTtX+7AlS8ryVSJSABYtgkGD4O9/h3nzYMAA+Otf045KRFKWU9LJ3T8HzgAwszWAlu7+93wGVkjUvU5ERETS5u7vJbObuPsyiSczGwi8UvtR1YCnnoIePap92BNPxLRRoxqOR0RWzHnnReumffaBq66CTTdNOyIRKQC5jl73XzNrZWZtgA+BO83shvyGVjhMo9eJiIhI4Ti2gnXH1XoUNWHpUiguhgULqn3oLUna7ZRTajgmEcmNOwwbBqNHx/I558Cbb8IzzyjhJCK/yHX0utbuPg/4A/CAu28D7Jq/sApLkZm614mIiEiqzOxQM3sKWN/Mnsy6jQDmpB3fCvn665gOGOLjafsAACAASURBVFDtQzN5qvXWq8F4RCQ3r70Gv/sdHHQQ3HprrOvYEXbYId24RKTg5FrTqbGZrQscAlyQx3gKkgGlyjmJiIhIut4DZgHrAYOz1s8H6uaowv/+d0x79qzWYe7w3Xcx4rq614nUoo8/jm50L74YSaZ77lmhpLGINBy5Jp0uA14E3nL3982sK/B1/sIqMGZpRyAiIiINnLt/C3wLvJx2LDVmxIiY7rxztQ678sqoU7xoUR5iEpHKPfUUvP8+XH89nHYaNGuWdkQiUuBy6l7n7o+7+2bufkqy/I27H5jf0ApHJuWkLnYiIiKSFjN7PZn+aGazs24/mtnstOOrNnf4739jvkWLnA8rLYWLLor5f/yj5sMSkSzTp8PAgTB8eCyfcw6MHw9nnaWEk4jkJNdC4uuZ2VNm9kNyG2ZmDaYHfVHS0kk5JxEREUnRTsm0LdAu65ZZrlvOPjumRx+d8yGzZpV1p9tggxUa9E5EcjFvHlx8cfyh3X47jBkT61u0gNVXTzc2EalTci0kfi8wHGif3J5J1jUImd51pco6iYiISErcvTSZ7Qg0cvcSYFvgJKB5aoGtqLFjYzpkSM6HbLZZ2fzHH9dwPCISHnggkk2XXQZ77QWffw7nn592VCJSR+WadGrn7ve6e/H/Z+++46uu7j+Ov87NXpCEAAHC3ntFQECGE9SKWBQUi6BWS7XuVmqtWlorjqp1tQXFUS3KTxxUQaUCBaqMgICMIFsSVhIyyB73/P74JiFAEgIk+Sbwfj4e93Hu93zP93s/CQFuPveczyl+vEV9/ETtDJUur3M1ChEREREAPgGsMaY9zoeAHYF/uRvSacrPh88/d2o5+flV+bLQUAgOdpbYncaKPBE5laIiKCg49rx3b6d209y50KmTu7GJSL1W1aRTijHmZmOMT/HjZpzdU84LHo+W14mIiEid4bXWFgDXAS9ba+8HWrgc0+lJSHDatm1P6zKPB66+Wnu8iFQba2HBAujbF14t3hRz8mT4z38gNtbV0ETk3FDVpNOtwA3AQeAAMA6YXEMx1VlaXiciIiJ1QKEx5nrgZ8BnxX1Vny5UF6xb57SjRlX5EmshPr6G4hE5H61cCSNGwFVXOVtBtmnj9CurKyLVqKq71+211l5jrW1srW1irb0WOH92r9O/uyIiIlJ33IpTVPwZa+0uY0xbYI7LMZ2eZ5912oEDqzQ8P9+Z5VTyXETO0qOPwoUXOrXVXnsNtm6Fa691OyoROQf5nsW1DwAvVlcgdZlBy+tERESkbrDWbjLG3AN0MMZ0AXZYa590O64q83ph9WrnecuWVRo+YsSx49mzayYskXPevn1OUbRGjeDyyyEwEO67TwXSRKRGVXV5XXnOm/k/xSWdsColLiIiIi4zxlwE7ADeAGYDPxhjhrgb1Wko2XbugQeqNPyf/4Rvv3Wex8dDREQNxSVyrjpyBH79a+jYEf70J6dv2DBntpMSTiJSw85mplOlGRhjzGzgauCwtbZHOecnAg/jJK+OAlOttRuKz+0p7isCCq21rlaxK1le51XOSURERNz3AnCltXYLgDGmK/BPoH5U/f3uO6e97LIqDf/Pf5x2715o1aqGYhI5F2Vnw0svwYwZkJEBN98M997rdlQicp6pNOlkjDlK+cklAwSd4t5vAa8A71Rwfjcw3FqbaowZDcwEyi7sH2mtTT7Fa9SKY8vrlHUSERER1/mXJJwArLVbjTH+bgZ0WkqKiPftW6Xhixc7bZMmNRSPyLnqvvtg1iynUPif/wy9erkdkYichypNOllrw870xtbaZcaYNpWc/6bM4Uog5kxfq6aZ0uV1IiIiIq5bZ4z5O/Bu8fFE4DsX4zk9773ntE2bnnLojh2wfz+MGeOUnxGRSlgL8+ZBjx7QpQs8/DD87Gdw0UVuRyYi57GzqelUnW4DFpY5tsBXxpi1xpg7XIqplDEqJC4iIiJ1xi+AXcBvih+7gDtdjaiqli+H9PQqr5MbP95pL7mkBmMSORcsWeLsBnn99c5udADt2yvhJCKuO5uaTtXCGDMSJ+k0tEz3UGttojGmCbDIGBNvrV1WwfV3AHcAtKqhhf4lFdO1vE5ERETcZIzpCbQHPrbWPuN2PKftnnucdtGiUw5NTj62Eu+uu2owJpH6bP16mDYNvvwSYmKc7R0nTXI7KhGRUq7OdDLG9AJeB8ZYa1NK+q21icXtYeBjYEBF97DWzrTWxlprYxs3blxDcZa8Vo3cXkREROSUjDGPAJ/gLKdbZIy51eWQTk9urvMLcrNm0KnTKYfv2eO0v/41eOrK3HyRuua992D1anj2WfjhB5gyBXx83I5KRKSUa/+FG2NaAR8BP7PW/lCmP8QYE1byHLgc2OROlA5PyfI6N4MQERGR891EoJe19nrgAmCqy/Gcns2bnXbKlCoNnznTaUeMqJlwROqlw4edGYMlswUffRR27YKHHoKgU+3zJCJS+2pseZ0xZg4wAogyxiQAjwN+ANbavwOPAY2A14prJhVaa2OBpsDHxX2+wL+stV/UVJxVUTLTyaupTiIiIuKePGttFoC1NskYU7/m/5TUmbn88ioN9y1+lzpqVA3FI1KfHD0Kf/kLPPecM2swJgYuuwwaNnQ7MhGRStVY0slae+Mpzt8O3F5O/y6gd03FdSaO1XRyNQwRERE5v7UzxnxU/NwA7cscY629zp2wqmjHDqcdOrTycWU0bqyldSK89Rb85jeQlATjxsGf/gSdO7sdlYhIlbheSLxeKF1ep6yTiIiIuOanJxy/4koUZ+roUWcbOtWbETk1r9f5xNvHB7KyoGdPmDEDLrjA7chERE6Lkk5V4Cmd6uRqGCIiInIes9Z+7XYMZ+W772D8+CoNtRb+9jcID6/hmETqGmvhiy/gt7+FX/4S7rgDpk51npfU/BARqUc0YbkKTPECO6+STiIiIiKnb8MGpw0MrNLwoiKnbdmyhuIRqYtWroSRI+HKKyEzE6KjnX6PRwknEam3lHSqgpJ/47W8TkREROQMzJ7ttHfdVaXh2dlOe8MNNRSPSF3z8MNw4YWwdSu88gps2QLXXON2VCIiZ01JpypQIXERERGpa4wxAWdwzShjzDZjzA5jzLRKxv3UGGONMbFnF2Wx1FSnja3a7d55x2mDg6vl1UXqpoQESE93nl98MUyfDjt3OslZf393YxMRqSZKOlWBp7SQuIiIiIi7jDEDjDHfA9uLj3sbY16uwnU+wKvAaKAbcKMxpls548KAe4FV1RZ0ZiZ0717lJULbtzvtbbdVWwQidceRI85udB07wtNPO31XXAG//z2Ehrobm4hINVPSqSqK3x95VdRJRERE3PcScDWQAmCt3QCMrMJ1A4Ad1tpd1tp84H1gTDnj/gg8DeRWT7jAl19WedpSQgK89JLzPOC053KJ1GHZ2U6SqX17eO45Z/3oHXe4HZWISI1S0qkKVLZPRERE6hCPtXbvCX1FVbiuBbCvzHFCcV8pY0w/oKW19vOzC7GMvDznl+2wsCoNj4tz2ttvr3LdcZH6YepUmDYNhgxxiuu//Ta0aeN2VCIiNUpJpyooXV6niU4iIiLivn3GmAGANcb4GGPuA34425saYzzA88CDVRh7hzEmzhgTl5SUVPngAwecdvjwU8bw6qtw003O81/96pTDReo2a2HePNi1yzmeNg2WLYPPPoOePd2NTUSklijpVAUl5Qe8yjqJiIiI+6YCDwCtgEPAoOK+U0kEWpY5jinuKxEG9ACWGmP2FN93fnnFxK21M621sdba2MaNG1f+qps2OW379qcMcP16Z3f46dOh20nVpkTqkSVLYNAgGDcOXnvN6evaFS66yN24RERqma/bAdQHJUknpZxERETEbdbaw8CEM7h0DdDRGNMWJ9k0AbipzH3TgaiSY2PMUuAha23cWQX81VdO279/pcO8Xnj9dWjc2KmnLFIvrV/vzGj68kuIiYE33oBJk9yOSkTENUo6VYGhZHmd0k4iIiLiLmPMLMr5LMxaW2lFYmttoTHmbuBLwAeYba3dbIyZDsRZa+fXSMA7dzpt27YVDpk/Hz7+2HneqlWNRCFSO2bNgtWr4Zln4O67ISjI7YhERFylpFMVaKaTiIiI1CH/KfM8EBjL8QXCK2StXQAsOKHvsQrGjjjD+I63YIFTOLmSreieew5WrXISTk89VS2vKlI7Dh+GP/4Rxo+HoUOd508+CeHhbkcmIlInKOlUBcZoppOIiIjUDdbaD8oeG2P+CaxwKZzKvfii0zZtWukwa53f17/+uhZiEqkOR4/CX/7iZExzc6FdO+eHODLS7chEROoUFRKvguKJTtq9TkREROqitkDlWR23ZGQ47axZFQ6ZORP27KmdcESqxezZTmH8P/wBRo+GzZvh/vvdjkpEpE7STKcq0PI6ERERqSuMMakce1viAY4A09yLqBI//OBsR1fB7I/MTLjzTue91rhxtRybyOnwep3W44GUFOjRA2bMgAED3I1LRKSO00ynKvCULq9zORARERE5rxlnzX9voHHxI8Ja285aO9fdyCqQnHzsl/VylJx67jl44YVaiknkdFgLCxdCv34wZ47T98ADzlpQJZxERE5JSacqKFle51XWSURERFxknQKTC6y1RcWPuv3m5KuvYNAgt6MQOTOrVsHFF8OVVzo1nBo2dPp9fI4thRARkUop6VQFpcvr6vbbOhERETk/rDfG9HU7iFMqKNCbJ6m/HnzQSZhu2QKvvAJbt8LVV7sdlYhIvaOaTlVQunudqjqJiIiIS4wxvtbaQqAvsMYYsxPIwpmUba21/VwN8ET//rfTXnedu3GIVFViIoSHQ0gIXHSRM7PpgQcgNNTtyERE6i0lnapAu9eJiIhIHbAa6Adc43YgVfLll047aVKFQ7Ztq6VYRCqTmuoUBX/pJXj0Ufjd7+Daa52HiIiclRpdXmeMmW2MOWyM2VTBeWOMeckYs8MYs9EY06/MuVuMMduLH7fUZJynYlRIXERERNxnAKy1O8t7uB3cSVaudNqmTSscsnCh0/buXQvxiJwoJweeeQbatYNnn4Xrr4eJE92OSkTknFLTM53eAl4B3qng/GigY/FjIPA3YKAxJhJ4HIjF2RJ4rTFmvrU2tYbjLVfpTCctrxMRERH3NDbGPFDRSWvt87UZzCkZAx06nHIIwIgRNR+OyEmmTIEPPoCrroI//xl69XI7IhGRc06NznSy1i4DjlQyZAzwjnWsBMKNMc2AK4BF1tojxYmmRcComoy1Mp7i75JmOomIiIiLfIBQIKyCR92yYYN+iZe6xVr46COndhPAb38L//0vfPaZflZFRGqI2zWdWgD7yhwnFPdV1O8KUzzXyausk4iIiLjngLV2uttBVElOjtNWsrQOICGhFmIRAVi6FKZNg1WrnJpNf/qT1nWKiNSCGp3pVBuMMXcYY+KMMXFJSUk19CJOo5STiIiIuMicekgd8X//57Q9e1Y4JCEBZs50npv685VJfbNhA1x5JYwc6cxweuMNeOIJt6MSETlvuJ10SgRaljmOKe6rqP8k1tqZ1tpYa21s48aNayRI7V4nIiIidcAlbgdQZY8/7rQ331zu6f37oWXxO72bbjpWykCk2r34olPU/tln4Ycf4NZbwdftxR4iIucPt/+Lnw9MKt7FbhCQbq09AHwJXG6MiTDGRACXF/e5wmOOlRIXERERcYO1trI6mXXLnj3QujWElV9qal9xEYUrroD33qu9sOQ8cPgw3HMPrF3rHD/9NOzaBQ89BEFB7sYmInIeqtE0vzFmDjACiDLGJODsSOcHYK39O7AAuBLYAWQDU4rPHTHG/BFYU3yr6W6+0SrJOXmVcxIRERGpXGam0w4desqh995bw7HI+ePoUXj+eXjuOaemWMeO0L8/NGnidmQiIue1Gk06WWtvPMV5C9xVwbnZwOyaiOt0lRQS1/I6ERERkVNITXXaYcMqHLJiRS3FIueH11+HRx6BpCT46U+dIuFdurgdlYiI4P7udfWCp6SQuLJOIiIiIpX7/HOnbdCgwiFxcU7bqVMtxCPnJq/XWY5gjFOVvnt3mDEDBg50OzIRESnD7ZpO9YOW14mIiIhUzbJlTnv11RUO8XigQwdo376WYpJzh7WwcCH06weffOL0PfooLF6shJOISB2kpFMVlC6vUyFxERERkcr5+4OfH4SGuh2JnGtWrYKRI+HKKyEjw/lZA2c3utKNf0REpC5R0qkKtHmdiIiISBXt2uVMYxKpTvfeC4MGwdat8PLLEB8PV13ldlQiInIKqulUBR5TMtNJRERERCq1fDn07u12FHIuSEyERo0gMBAuvNB5fv/9EBbmdmQiIlJFmulUBaa0ppPSTiIiIiIVWrzYaUePdjcOqd9SU+Hhh50Zc6+95vRNmACPPaaEk4hIPaOZTlVQurpOOScRERGRin3wgdNOnepuHFI/5eQ4S+eeegrS02HiRBg71u2oRETkLCjpVAUlM52UcxIRERGpxJo1TtuqlbtxSP10003OjnSjRzuJJy3TFBGp97S8rgpMSU0nTXUSERERqdiePdCzp9tRSH1hLXz8MRw+7Bw/8ggsXQoLFijhJCJyjlDSqQq0vE5ERETkFPLznVo8F11U6bDCQvjXv8DrraW4pG76739h8GC47jr4+9+dvgsugOHD3Y1LRESqlZJOVVA600kL7ERERETKl5DgtFFRlQ575RWnjYmp4Xikbtq4Ea66CkaMgH374PXXnRlOIiJyTlJNpyrwFE91Ss7MdzcQERERkbpq2zan7dOn0mFHjjjtp5/WcDxSNz35JHz7LTzzDNx9NwQFuR2RiIjUIM10qgJTvMDuNx9uJP5ghsvRiIiIiNRBGzc6bYsWlQ774gunDQ+v4XikbkhKgvvug82bneMXXoCdO+HXv1bCSUTkPKCkUxWEBPiUPt93JMfFSERERETqqH/8w2m7datwyM6dxza4k3Pc0aPwhz9Au3bOmsrly53+5s0hIsLd2EREpNYo6VQF7RqH8ubkCwBIzdYSOxEREZGTFBZC69YQGlrhkBUrnLakbrSco2bOhPbt4Ykn4IorYNMm+MUv3I5KRERcoKRTFcW2cT6RSVPSSURERORkubkwenSVhl5+eQ3HIrXP6z221fPOndC9O6xcCR9+CF26uBubiIi4RkmnKgoN8MXXY0jNLnA7FBEREZG6pajIqd2jGj3nH2udQl39+8NXXzl9f/oTLF4MAwe6G5uIiLhOSacqMsYQHuyvmU4iIiIiJ0pOdlqv1904pHatXg0XX+zMcEtPP/bn7+cHxrgbm4iI1AlKOp2GiGA/UrM000lERETkODnFG6307l3hEGth0aJaikdq3l13OTOZNm+Gl16C+PgqL68UEZHzh6/bAdQnEcH+KiQuIiIicqKMDKcNDKxwyLp18N57zvOwsFqISarf/v3QuLEzkyk2Fh5/HB58UH+gIiJSIc10Og0Ng/1IU00nERERkePt3Om0fn4VDvnf/5z2zTchKqoWYpLqk5oK06Y5O9LNnu30TZni7E6nhJOIiFSiRpNOxphRxphtxpgdxphp5Zx/wRizvvjxgzEmrcy5ojLn5tdknFUVEeynmU4iIiIiJ8rNddoePSocsmSJ0w4ZUgvxSPXIyYFnnnGSTc88Az/9KVx2mdtRiYhIPVJjy+uMMT7Aq8BlQAKwxhgz31q7pWSMtfb+MuN/BfQtc4sca22fmorvTEQE+5OWXYC1FqPiiCIiIiKOb75x2vDwCof4+UHXrtCxYy3FJGfvpz+FhQudWk1PPVVpzS4REZHy1ORMpwHADmvtLmttPvA+MKaS8TcCc2ownrMWHuxPfpGX7Pwit0MRERERqTt8iz/HjI52Nw45O9bCJ584y+kAHnnEmaK2YIESTiIickZqMunUAthX5jihuO8kxpjWQFtgcZnuQGNMnDFmpTHm2poLs+oigp06BVpiJyIiIlLGkiUQGel2FHI2/vtfGDwYxo6FWbOcvqFDYcQIV8MSEZH6ra4UEp8AfGitLTuFqLW1Nha4CXjRGNO+vAuNMXcUJ6fikpKSajTINlEhAMQfOFqjryMiIiJSrzRoAKGhlQ5JS3Mm0kgds2EDXHmlk1zat89JOD3wgNtRiYjIOaImk06JQMsyxzHFfeWZwAlL66y1icXtLmApx9d7KjtuprU21lob27hx47ONuVJ9Wobj7+th5a6UGn0dERERkXolLg569qzwdGYmLFoE2dm1GJNUzaOPwrffwtNPw/btcPvtx5ZLioiInKWaTDqtAToaY9oaY/xxEksn7UJnjOkCRADflumLMMYEFD+PAoYAW068trYF+vnQt2U4y7cnU+TVR3UiIiIiABQVwdGKZ4JnZjrtlVfWUjxSsaQkuP9+2LnTOX71Vdi1C37zGwgKcjc2ERE559RY0slaWwjcDXwJbAXmWms3G2OmG2OuKTN0AvC+tcdNuO4KxBljNgBLgBlld71z06B2jdh26ChDn15MboEKiouIiIhQVAQXXVTh6ZKkk2pRuygzE6ZPh/bt4aWXYOlSp79VK4iIcDU0ERE5d9Xo3Flr7QJgwQl9j51w/EQ5130DVDxH20WTB7cht6CIfyzbxVdbDnFN7+ZuhyQiIiJSJcaYUcBfAR/gdWvtjBPOPwDcDhQCScCt1tq9ld7U63WSTpXMkvnd75y2YcOzCF7O3D/+AY89BocPw3XXwZNPQpcubkclIiLngbpSSLzeiAjx5+FRXWjeMJAP1ya4HY6IiIhIlRhjfIBXgdFAN+BGY0y3E4Z9B8Raa3sBHwLPnPLGeXlOW0EdoPh4mDvXeT5+/JlELmek7CKC77+Hrl1h5UqYN08JJxERqTVKOp0Bj8cwLrYly7cnsTMp0+1wRERERKpiALDDWrvLWpsPvA+MKTvAWrvEWltS7nslzkYwlUtPd9rBg8s9vWKF0z75JHj0zrPmWQtffgn9+8Py5U7fX/4CS5bAwIHuxiYiIucd/dd/hiZd2Bp/Hw+vLdnpdigiIiIiVdEC2FfmOKG4ryK3AQvLO2GMucMYE2eMictPKd7Vt4KkU4lJk04nVDkja9bAJZfAqFGQlgY5OU5/QAAY425sIiJyXlLS6QxFhQZwy+A2zFuXwOvLd7kdjoiIiEi1McbcDMQCz5Z33lo701oba62N9ff3Bz8/8PEp917r1tVgoHLMnXfCgAGwaZNTKDw+Hi6/3O2oRETkPKek01l4eFQXLu7ShBcW/UBGboHb4YiIiIhUJhFoWeY4prjvOMaYS4HfAddYa/NOeVdrYdCgCk8vWuS0DRqcVqxSFQcOOEXcwdka8IknYOdO+NWvwN/f1dBERERASaez4uMx3H9pJ7Lyi+j1xFfMU2FxERERqbvWAB2NMW2NMf7ABGB+2QHGmL7AP3ASToerdNe8PAgMLPdUZibs2AGXXaakU7VKS4Pf/hbat4c5c5y+X/4SHn8cwsLcjU1ERKQMJZ3OUs+YhlzcpQkAs7TMTkREROooa20hcDfwJbAVmGut3WyMmW6MuaZ42LNAKPB/xpj1xpj5FdzumIICJwlSjrg4p23e/KzDF3BqND37LLRrBzNmwNixp6ylJSIi4qby97aV0zJ78gX8belOnv4inun/3sLkwW1o1SjY7bBEREREjmOtXQAsOKHvsTLPLz3tm3o80L17Ba/ntFOmnPZdpTw/+Ql8/bVTKPypp6BPH7cjEhERqZRmOlWT4Z0aAzD7f7v50+dbXI5GREREpBZpSVfNsBbmz3fWKYKzpG7xYli4UAknERGpF5R0qiZdm4XRt1U4AGv2HKGwyOtyRCIiIiK1wNoKd65bv76WYzmXLFsGQ4bAmDEwe7bTd8klMHKku3GJiIicBiWdqokxho9/OYRXb+pHanYBcXtT3Q5JREREpHZUkHT6z3+cNiamFmOp7zZuhKuuguHDYe9emDnTKRIuIiJSDynpVM2Gd25MiL8PH6zZ53YoIiIiIjWvkplO/v7Qs6ezyZpU0QMPwDffOIXCt2+Hn/8cfFWGVURE6iclnapZaIAvN1zQkn9v2M/+tBy3wxERERGpWZUknQCMqcVY6qOkJCfRlJDgHM+cCTt3wsMPQ7A2phERkfpNSacacOuQtng8hmkffc/KXSlYayks8nI0t8Dt0ERERESqXwVJp+XLj+1gJyfIzITp051pYH/9q7MrHUC7dhAZ6W5sIiIi1URzdWtAy8hgHhndhSf+vYVlPyQxpk9zQgJ8+c+WQyz7zUgC/Sr+NFBERESk3ikn6XToEKSkQECAC/HUdX//Ozz+OBw+DGPHwpNPQteubkclIiJS7ZR0qiGTh7RlUPtGfLHpIC/+Z3tpf9/pixjVI5oXxh+/zW16dgHPfbWNhy7vTMNgv9oOV0REROTMeU6ePP/dd077u9/Vcix1lbXH1hquXu0kmT79FAYNcjcuERGRGqTldTWoS3QD7ru0E5MubE14cSIpp6CIj79LZPn2pOPGPr9oG/9cuZdP1ie6EaqIiIjImSsn6fTVV07br18tx1LXWOt8My64AOLinL7XXoMlS5RwEhGRc56STrVg+pgerPztJcyeHMv9l3YiJiKIp7+Ix+t1ihzsO5LN598fACAhNdvNUEVEREROX6dOxx1u3AgvvAB+fud5XmXNGrj0UrjiCmetYVqa0x8YqArrIiJyXtDyuloS6OfDxV2acnGXprSMDOKBuRt46P82sPXgUbYeyCgdt6XMcxEREZF6we/40gC33ea0P/uZC7HUFbfdBrNnQ+PGTqHwO+9UgSsRETnvKOnkgjF9WvDeqh/5ZH0iPWPC+f3V3WjeMJD/bD3M1/GHsNZi9OmXiIiI1Bf+/scdZhR/hjZzpguxuOnQIWjSxJnF1LWrUyz8wQchLMztyETkHFdQUEBCQgK5ubluhyL1XGBgIDExMfj5VU+taSWdXODjMcybOvik/uTMPOataFSobQAAIABJREFUS2DH4Uw6NtWbExEREamfPB644YZyN7U7N6WlwTPPwIsvwrvvwnXXwUMPuR2ViJxHEhISCAsLo02bNprAIGfMWktKSgoJCQm0bdu2Wu6pmk51yCVdm9Ig0Jeb31jFz9+JIy07/7jz6/elcfioMtciIiJSx1TTG9N6JzcXnnsO2rWDp56CsWOhT59TXyciUs1yc3Np1KiREk5yVowxNGrUqFpnzNVo0skYM8oYs80Ys8MYM62c85ONMUnGmPXFj9vLnLvFGLO9+HFLTcZZVzQPD+LVif2IbhDIf7clcfvbcaRk5gGQnlPA+H98y+8/2eRylCIiIiInKDOlqbAQ4uNdjKU2XXYZ/PrXMHAgfPcdvPeek4ASEXGBEk5SHar756jGkk7GGB/gVWA00A240RjTrZyhH1hr+xQ/Xi++NhJ4HBgIDAAeN8ZE1FSsdclFHRvz6d1DeWF8HzYmpjPiuaX85attfLo+kbxCL19vPVyaiBIRERGpa775xmnzzsW3K9bCZ585M5wAHnkEliyBhQs1w0lEzmsjR47kyy+/PK7vxRdfZOrUqZVeFxoaCsD+/fsZN25cuWNGjBhBXFxcpfd58cUXyc4+thP8lVdeSVrJjqHVoE+fPkyYMKHSuPbs2UOPHj1Kj1evXs2wYcPo3Lkzffv25fbbbz8uxjOxe/duBg4cSIcOHRg/fjz5+fknjcnPz2fKlCn07NmT3r17s3Tp0tJza9eupWfPnnTo0IF77rkHa+1ZxVMVNTnTaQCww1q7y1qbD7wPjKnitVcAi6y1R6y1qcAiYFQNxVknXdWrGZ/8cggXdYzi5cU7eOzTzUSF+lPotby78ke3wxMRERE5psynort3O+0DD7gUS01ZvhyGDIGf/ATeecfpGz0aRoxwNSwRkbrgxhtv5P333z+u7/333+fGG2+s0vXNmzfnww8/POPXPzHptGDBAsLDw8/4fmVt3bqVoqIili9fTlZWVpWuOXToENdffz1PP/0027Zt47vvvmPUqFEcPXr0rGJ5+OGHuf/++9mxYwcRERG88cYbJ42ZNWsWAN9//z2LFi3iwQcfxOv1AjB16lRmzZrF9u3b2b59O1988cVZxVMVNZl0agHsK3OcUNx3op8aYzYaYz40xrQ8zWsxxtxhjIkzxsQlJSVVR9x1RrfmDXhtYn/+fnM/pgxpwys39ePKntG8tHg7V/51Oct+qNrXm5lXyKTZq4k/mFHDEYuIiMh5qUzS6a23nDYqyp1Qqt3338PVV8OwYbB3r7Ml3623uh2ViEidMm7cOD7//PPSmTd79uxh//79XHTRRWRmZnLJJZfQr18/evbsyaeffnrS9WVnCeXk5DBhwgS6du3K2LFjycnJKR03depUYmNj6d69O48//jgAL730Evv372fkyJGMHDkSgDZt2pCcnAzA888/T48ePejRowcvvvhi6et17dqVn//853Tv3p3LL7/8uNcpa86cOfzsZz/j8ssvLzf28rz66qvccsstXHjhhcd9j5o2bVql68tjrWXx4sWlM8JuueUWPvnkk5PGbdmyhYsvvhiAJk2aEB4eTlxcHAcOHCAjI4NBgwZhjGHSpEnlXl/d3N697t/AHGttnjHmTuBt4OLTuYG1diYwEyA2Nrbm54a5YFSPZozq0QyA7s0bEOy/hVW7U7jzn2vp0aIBV/Vsho+Ph6O5BUwd3v6kNZird6ew7IckujdvQJdRDdz4EkRERORcVvzeIysL1q2DZs2gW3lFFeqjX/wCtmyBGTPgV7+C4GC3IxIRqdR998H69dV7zz59nA06KxIZGcmAAQNYuHAhY8aM4f333+eGG27AGENgYCAff/wxDRo0IDk5mUGDBnHNNddUWDvob3/7G8HBwWzdupWNGzfSr1+/0nNPPvkkkZGRFBUVcckll7Bx40buuecenn/+eZYsWULUCZ94rF27ljfffJNVq1ZhrWXgwIEMHz6ciIgItm/fzpw5c5g1axY33HAD8+bN4+abbz4png8++IBFixYRHx/Pyy+/zE033XTK79emTZu45ZZTl6betm0b48ePL/fc0qVLj5utlZKSQnh4OL6+ThonJiaGxMTEk67r3bs38+fP58Ybb2Tfvn2sXbuWffv24fF4iImJKR1X0fXVrSaTTolAyzLHMcV9pay1KWUOXweeKXPtiBOuXVrtEdZDYYF+PHd9bw6m5zJj4VbiDx7liX9vKT2fm19Et+YNCPDzYUCbSHw8hrV7UwFYs/uIW2GLiIjIeaB5c8jIgP793Y7kLCQnw9NPOwXCmzSBN9+Exo0h4rwoLyoicsZKltiVJJ1Kln5Za3nkkUdYtmwZHo+HxMREDh06RHR0dLn3WbZsGffccw8AvXr1olevXqXn5s6dy8yZMyksLOTAgQNs2bLluPMnWrFiBWPHjiUkJASA6667juXLl3PNNdfQtm1b+hTX4+vfvz979uw56fq4uDiioqJo1aoVLVq04NZbb+XIkSNERkaWmzQ73SLcnTt3Zn01ZwhvvfVWtm7dSmxsLK1bt2bw4MH4lNnwo7bVZNJpDdDRGNMWJ4k0ATguJWiMaWatPVB8eA2wtfj5l8CfyxQPvxz4bQ3GWu9ENwzkxQl9sdayNyWb/CIvTy3YykuLd5SOCfLzIb/IS5HXmQC2MSGd3IIi1v2Yyto9qfxyZAd8PNrhQERERM6SMXi9TsIJYO5cd8M5I5mZ8MIL8OyzzpStvn3hppugUye3IxMROS2VzUiqSWPGjOH+++9n3bp1ZGdn07/4E4j33nuPpKQk1q5di5+fH23atCG3ZEOG07B7926ee+451qxZQ0REBJMnTz6j+5QICAgofe7j41Pu8ro5c+YQHx9PmzZtAMjIyGDevHn8/Oc/p1GjRqSmppaOPXLkSOlMq+7du7N27VrGjKm8rPXpzHRq1KgRaWlpFBYW4uvrS0JCAi1anFyFyNfXlxdeeKH0ePDgwXTq1ImIiAgSEhJK+yu6vrrVWE0na20hcDdOAmkrMNdau9kYM90Yc03xsHuMMZuNMRuAe4DJxdceAf6Ik7haA0wv7pMTGGNoExVCp6ZhzJoUy7/vHspnvxrKe7cP5Kf9W9C/lZO3Cw3wJb/Iy42zVnLTrFX8ZdEPfPzdsYlnW/ZnsO/I2VXSFxERkfOUMfzzn87TP/yhHtZz+tvfoEMHeOwxuOQS2LTJSTiJiEiVhYaGMnLkSG699dbjCoinp6fTpEkT/Pz8WLJkCXv37q30PsOGDeNf//oX4CxT27hxI+AkfEJCQmjYsCGHDh1i4cKFpdeEhYWVW6T7oosu4pNPPiE7O5usrCw+/vhjLrrooip9PV6vl7lz5/L999+zZ88e9uzZw6effsqcOXMAZ/e6d999t3QHuLfffru0ptTdd9/N22+/zapVq0rv99FHH3Ho0KHjXqNkplN5jxMLoRtjGDlyZGnB9bfffrvcpFbJ1wqwaNEifH196datG82aNaNBgwasXLkSay3vvPPOKZNi1aFGazpZaxcAC07oe6zM899SwQwma+1sYHZNxneu8fXx0DOmYenxkA5RZOcXMm3e99wyuDUfrk3kq80H+dXFHVj2QxK//2QTS7cd5qaBrbjtrTgahwWw6IFhBPj6sO9INn/8bAv3XtqR7s0bVvKqkJFbwPh/rOTRq7oypEN9e5cpIiIiZ80Y0tOdp/Wmxra1xwqgL10KnTvDxx9DmaKvIiJyem688UbGjh173E52EydO5Cc/+Qk9e/YkNjaWLl26VHqPqVOnMmXKFLp27UrXrl1LZ0z17t2bvn370qVLF1q2bMmQIUNKr7njjjsYNWoUzZs3Z8mSJaX9/fr1Y/LkyQwYMACA22+/nb59+5a7lO5Ey5cvp0WLFjRv3ry0b9iwYWzZsoUDBw5wxx13EB8fT+/evTHGEBsby1NPPQVA06ZNef/993nooYc4fPgwHo+HYcOGMWrUqFN/Eyvx9NNPM2HCBB599FH69u3LbbfdBsD8+fOJi4tj+vTpHD58mCuuuAKPx0OLFi34Z8mnQsBrr73G5MmTycnJYfTo0YwePfqs4qkKU5KVOxfExsbauLg4t8OoF/YkZ/G3pTv5ZH0ieYVeAv085BZ4aRsVQrfmDdiyP4PdyVk0axjIp3cNIaegqLQ21Jv/28PgDo347eiuACz8/gBT31vHhe0aMeeOQW5+WSIico4zxqy11sa6HYccE2uMjdu9m5fmt+HeeyElBSIj3Y6qEtbCokXwu9859Zp69IDsbAgKOm4XPhGR+mTr1q107drV7TDkHFHez9OZvgdze/c6cUmbqBCeHteLX45szzc7U4htHcHi+MOs3JXC+h/TCPL34fGfdOOZL7Yx5a01pGUXkJiWgzEQFuDLpv3pbD1wlH6twjl8NA+Ab3el8OqSHXy4NoEgPx+Gd27MxoQ0Xp90AUH+p1+4bGNCGhHB/rSM1C4xIiIidZ1bNUROy5o1MG0aLF4MrVtDUpLTrx3pREREaoSSTue51o1CaN3IqeTfsWkYdw5vf9z5mIhgHpy7Ho/HMKhdJLuSsvjXzwdx7av/Y9kPSSz7wXmzNqBNJAczcnn2y210ahrKruRMthxwqon+7pPvGdcvhi0HMgjy9+Gqns0ID/YHID2ngIZBfiyJP8xnGw/w5+t6EODrQ2ZeIRNnraJTdBjzpg6uxe+IiIiInLYyM4Tq5CZv1sLkyfDOO07BqRdfhF/8AsoUkRUREZHqp6STVOqybk3Z8PjlFHotvh5DboGXIH8fZk++AD8fw4rtybz97V5uvrA1Q9o34sO1CUy4oBWLtx3i/+IS6NqsAW+s2M1H644VLf9wbQJv3zqA3328iX9v2M9VvZrx9dZD5BZ48VrL1b2akZiWw9G8QtbuTWXDvjR6twwn6WgeDYP88Pd16t9ba097S0oRERGpAcbg8cDEiXVshVpyMjRq5ATVvr1TKPzBB6FBA7cjExEROS+oppPUuJW7UkjJzGdIh0Ysjj/MA3M3EBniT1p2PsM7NWbZ9mT6tQqnQaAfX8cfBiDA10PbqBB2JWeRX+ilecNA9qfnEuDr4apezTiSlc/m/Rm8NeUClv2QzK6kTO65pCMtI4Ox1pKeU8CmxAzaNg6hRXgQ4CSpCr0WP58a27TxrBV5LVe9tJwxfVowdUT7U18gInKeUU2nuifWGBv34490GNmSQYPg3XfdjghIT4dnnnFmNM2bB2dZuFVEpK5TTSepTqrpJPXKoHaNSp9f1y+GrLxC3lv1I49d3Y1r+7agyGvx8RhyC4pITMvh9eW7OZyRyx+v7cH+tBzW7Ell7d4jXB/bkkMZuXy+8QABfh6MMYx99Rvyi7x4DHyyPpHrY1vy321JJKbllL5mt2YNiAzxJyLEn6XbDhPb2pn3/8iVXWkZGcwf/r2FAW0juKBNJE8tjOeWC9swoG0kS7cd5t8bDnBxlya8u3IvLSODeOq6Xvh4DEu2HaZni4ZEhTrT8jPzClmz+wgjOjcmv8jLpsQMlm9Pom1UCJd0bUpoQPl/1XYcPsr2Q5mM7tkMgBU7kok/eJSs1Xv5xfB2VZ7JtTs5i+bhgQT4nn7trFP57w9JhAX60q/Vma2XyC/0ls5OExERqVG5ufDqq/DnP8ORI3DjjdCpk9tRiYiInLc000nqrX1HsnljxW58PYZbh7blpa+3MzduHy0jg7lpQCs6R4ex5UAGK7Yns/VABqnZBcREBJGTX4TXWoq8lu7NG/LtrhTAKZB+NK8QgKYNAsgt8JKeUwBAdINADmbkcn3/GC5oG8lvPtxIu6gQHr+mO5HB/ry6ZAdfbD5Ii/AgsvMLyS3wklNQBEDbqBDuv6wTX2w6gI/Hw4C2kVzVsxn703KY+Poq0nMKmD6mO60bhfDGit2ldbJuHNCKq3s1o8hrOZpbSKCfh8y8QjJyChjcIYqwQF8ig/2JP3iUMa/+j1Hdo/nrhD5k5hWW1swCWL8vjcTUHK7q1azC72V2fiHLfkhiROcm7E/LYdGWQ/Ro0ZCYiCCGP7sUgPsu7ch9lx7/xv1obgG7k7PoFRMOUJpALLExIY2Js1bx5HU9uaZ3c86EtZbPvz9AWnYBUaH+7EnJZkyf5uQXeoluGMj3Cen0aNGQQD8f0nMK8BjwGENIBYk+EanfNNOp7ok1xsbt20eHETHuznSyFgYNgtWr4Yor4KmnoG9fl4IREaldmukk1ak6Zzop6STnlITUbCKC/U9KOPyYks03O5O5IbYlHo8hITWb8f9YSUZuAQ9e1om8Qi/f7Exh8pA27DycyZebD7IpMYO7L+5A0tE8po3uwmtLdvDS4h0AdGoayoH0XI7mFpa+RklfZIg/OflFfHr3EH44lMkDH6wnJSufiGA/gvx82J+ei7+PB39fDw2D/Ajw87ArKav0PrcPbcu/Vv9Idn7RKb/e0ABffDyGo7kFeC34+3jIL/LSrnEIjUMDsBbi9h7Ba2FMn+Z0jg5j7pp9pOUUMLpHNA0C/WgY7EfcnlQWxx+mecNA8gq9pGTlA9A4LICko3lc1DGK5duTmTiwFRHB/rRvEkLj0ED+smgb3/2YxnPX9+bDtftYuzeVkZ2bcG3fFjQPD+LphfF8uyuFRiH+/P7qbhgDLSODadMohIycAnIKivDzcZZSpmbnExnsj6dM0io1K5/H5m/m3xv2A8e+Pn9fD/mFXlqEB5GYlkOX6DCiGwaydFtS6fflqp7N2JWcyZAOUfj7etiflkOflhFsSkznJ72bExboy4LvD1BQ5CUzt5ArukdzJDufZg0DyS+05BUW0a91BKH+vqz9MZWVO1OIDPXniu7RhAf5kVNQRFigX4V/NqlZ+Xy6PpEbB7aqkRloIucrJZ3qnlhjbFxCAh2Gt6j9pJO18MUXcOml4OcHn3zi1Gu6+OJaDEJExH1uJ51SUlK45JJLADh48CA+Pj40btwYgNWrV+Pv71/Z5QBMmTKFadOm0blz5wrHvPrqq4SHhzNx4sRqifvQoUO0aNGCv//979x+++0AFBYWEhUVRVpaWum4119/nU2bNvFi8Vatb731Fs899xzGGPz8/PjZz37G/ffff1axLFiwgPvvv5+ioiLuvPNOfv3rX580Zs+ePdx6660kJyfTqFEj3nvvPZo3P/bhfnp6Ol26dGH8+PGlsZ4JJZ0qoKSTnI7cgiKModyEgLWWnIIigv2PT16t3JXCjynZXN69Kf6+HtbtTSMjt4BDGbncPKg1BiiylrxCLw2KExLZ+YVsSsygc3QYDQJ9iT94lA/W7GPL/gz+ckNvfDyGbQePEuTvQ1SoPx2ahHEwPZeCIi97UrII9POhQaAfeYVF+HgMfj4evk9IJzu/kG2HjpKRU8iEAS2Zv34/oQG+RIT4s25vamlCrFdMQ3w8hne+3UtOQRE9WzSkQ5NQ5m/Yj8dAQZHzb8CkC1uzfl8a+45k88/bBrIk/jDf7krhql7NGB/bkj9+toV3Vu7lxH8yWkUG8+ORbDwGxvWP4bONB45LmE0c2IovNh0sTWQB+HoMhV570nH7xiEMbNeIFuFBFHktry/fRXZ+EXdf3IF3V+4lM6+Qt6YM4NP1+ynyevm/tQkM7RDFviPZJGfmM3FgK8ICfXlv1Y8cSM+le/MGbN7v7KIYGuBLZt6xJGEJY5zXL/k+lNUwyA8/H0NyZv5x/SXJr0Yh/nRv0ZDhnRoztEMUKZl55Bd56RUTzpOfb2XeugSu6d2c/q0jaBsVwuL4wzQK8ee2i9oS7O+LtZZ1P6ax7eBRujYLw2stOfleFmw6wJU9mrFyVwobE9O5sF0jxvZtQV5hEVGhAZXO4qrs57rsmEMZubSKDC53Cae1luTMfAL8PKU/x6cjK6+Q9JwCiryWlpHahlyql5JOdU+sMfaDZYl0GNacCRNgzpxaeuEVK+Dhh+Gbb+Dtt2HSpFp6YRGRusftpFNZTzzxBKGhoTz00EPH9Vtrsdbi8dSd0hsvv/wyc+fOxd/fn6+//ho4ddLps88+44knnuCzzz4jOjqa3Nxc3n333dKk1ZkoKCigc+fOLFmyhOjoaGJjY5k3bx6dTlgiPnbsWMaNG8fEiRP56quvmDNnDm+++Wbp+bvuuovU1FSaNGmipFNNUNJJpGK5BUXFyTBfjDGkZxcQHOBDZm4hu5Kz6NfKWSJXUGQrrMF0JCufYH8ffjySTXpOAQ2D/GgeHsR/thyiRUQQF7SJJD27gMS0HH48kkWzhkH0bhlOQZGX7Ycy8fc1/G9HCntTsmkVGYSPx5CVX0RG8b0WbTnEruQsjhQnqEZ2bsy00V3pHB3GxoQ0MnMLGdwhqjSeg+m5NAkLwOMxx+1muO9INvvTchjYrhEJqdn4ejxEhfrz7a4UYiKCWRJ/mIIiL2P7tSDIz4fkzHy2HTxKm6hgEo7k4OfrwWNgblwC/j4ehnWK4uIuTUhMy+HrrYdJzykgPNiPvcnZrP0xlR2HM8v9fkWFBpCcmVd6HOjnIbfAi5+PoWuzBqRm57PvSE6515bo0CT0uPsbA20ahdA2KoSUzDwCfH1o3SiYvUey6RIdxqIth/D1MRgMzRoG4utj6NgkjC7RYUSG+LNoyyFW7EjmQPH3rldMOL1jGuLr48wG25WcyabEjNKlpX1bhRMZ7E+rRsEUFln6tAynfZNQ/HwM2flFbD+UyYZ9aTQLD+Tw0Tx+TMlmxY7k0nibNgigc3QDbhrQkohgfzo2DSM9p4AfDh3FYwzhwX70aRlOkdcp9L/8hyTmrUukbVQwfj4evtmZwsB2kfxyeAcaBpefAEvOzCM0wJdAv2OJthOXesq5Q0mnuifWGPv0O4lcOqk5L70Ev/pVDb/g99/DI4/AZ59Bs2bwxBNw663gq2XVInL+qqtJpx07dnDNNdfQt29fvvvuOxYtWsQf/vAH1q1bR05ODuPHj+exxx4DYOjQobzyyiv06NGDqKgofvGLX7Bw4UKCg4P59NNPadKkCY8++ihRUVHcd999DB06lKFDh7J48WLS09N58803GTx4MFlZWUyaNImtW7fSrVs39uzZw+uvv06fPn1OinXw4MG8/PLLjBs3jm+++YZmzZqdMuk0ePBgZsyYwbBhw6rte7Z8+XJmzJjB559/DsAf//hHAgMDT5rt1LlzZ5YuXUqzZs0oKioiMjKS9PR0wJlR9te//pWRI0ceNyvrTKiQuIictkA/n+N+KS/5BT4ixJ/+Icemu/r7VvyLemTxuE5Nw47rv7Zvi+Pu2zDYj27Nj21H7efjKT3u0OT4a8u6c7izY19WXiGp2fnERBybJVNSN6qs6IaBpc/LzthpGRlcOsOm7D0u6uhM8W07tO1x9wkL9KNtVAgAXaIbnDS+RJdov+POl0hIzeabnSk0Dg0g2N+HtT+mkplbyB3D2rEzKQtfjyEhNYdLujZh8/4MFm05xIZ9aTRtEMg9F3ekX+sItuzPoKC4CP3Ph7Ul/sBRwoP96NsqgrV7j7D1wFH8fT0cSMtl64EM9qRklS5//HTDfro2a8CHaxNo2iCQzLxCQoqTg6EBvqzZnUp+kRdwZm/1aNGAO4a1Y+3eVLYfyuQ/Ww8BEB7sR8uIYK7sGU2npmGkZuXzzc4UfjzifH1+PoZ/rtx70tdfUg+tYZCzhPSuke2JbhBIVn4RPxw8ytfxh0trlZXHY6DMxDcaBvmVxtS7ZTgzl+1izqofuaBNJB6PITLYn1W7Uwjy98XXY/g+MZ0mYQH86uIOrPsxDV+PYf6G/VzYvhEdm4QSFRpA0waBdGveoPRnd/P+dJIz83l9+S46Nw3j0m5N8VrLV5sP4bUWX48HP19D56ZhjO3bAmMM+YVenv0ynm2HMukT05AAPx+y8gppGxVCWnYBAX4e1u9LI7Z1JIVeL0M7RNEqMpjdyVlENwwkNMCXjFzn+1Se7PxCfD0e8gqL2HrgKNsOZpT+/Ewe0oaYiGAOZeRyOCOPHi0aVHmjAZHaUOR1fh4HDqzhF7IWbrkFdu1yajbdcw8Ea0aliMhx7rsP1q+v3nv26ePsCHoG4uPjeeedd4iNdfIVM2bMIDIyksLCQkaOHMm4cePo1q3bcdekp6czfPhwZsyYwQMPPMDs2bOZNm3aSfe21rJ69Wrmz5/P9OnT+eKLL3j55ZeJjo5m3rx5bNiwgX79+pUb1549ezhy5Aj9+/fn+uuvZ+7cudx7772n/Ho2b95M//79TznunXfe4fnnnz+pv3PnznzwwQfH9SUmJtKyZcvS45iYGDZs2HDStb179+ajjz7irrvuYt68eWRkZJCenl6a5Hv//fdZsGDBKWOrTUo6iUidExLgW68KgcdEBHND7LFfegaW2bGxf2snUde7ZXjxcQT9W5+8E2D7xqEAXFf8f2KzhkFl7hFJ/9aR5b62M00ZPB5DkdfiMZBX6MXXY/D1cWasFRR5SUx1ZjENbh9VmnycMsRJvu1NycLXx0OL8KCT7v/ACa+14PuDZOY5NcTCAn3pHRPuFNAvKCLE36fcREhmXiF7U7JIycxn8/4MIkOc5J1PcX219fvSCQt06pN1a9aAIR2ieHflXqIbBnJF92i27M9g1vJdbNmfQV6hs8vliM5NSM3KJzOvkF9f0ZlPvkvk959uJjTAl6z8Qvq3imDbwaOs3JVCboGTcDMG+rYMJ6fAS/zBDKx1Zp+t2JHM6yt2AxDk50Ogn4fCIktekZf8Qi9vf7uXdlEh7E7OYv2+NLpEh/HSYieJdmLCDOCjdYmlz0P8fcjKLyr9+tKyCxjbtwVRof6kZOWTmpXPkax8ohsGsmZPqvP9yi0sTRKGBviSX+jl9RW7aRwWQHpOAfmFXmIigrh5UGs6R4fh9VqaNQyiY9NQvvsxjU5NQykocqavZ+QWkpyZR2zrCO79YD2Bvj48dnW30qSztZbcAi9B/icvx/R6Lf/dnsSBtFwu7tLkuCQvODMfj2Tl06FJ6HH9qcVvvA+XAAAWzUlEQVQzFSNCTl27Qc4dL7/i/N2vQsmO05ecDM895yyli4hwikZFR0Nk+f8uiohI3dK+ffvShBPAnDlzeOONNygsLGT//v1s2bLlpKRTUFAQo0ePBqB///4sX7683Htfd911pWP27NkDwIoVK3j44YcBJ0nTvXv3cq99//33GT9+PAATJkzgl7/8Jffee2+FH+yd7gd+kyZNYlI1L/1+4YUXuPvuu3njjTcYPnw40dHR+Pj48PLLL3PttdceV9+prqg/v9WJiMhJjDGU/P/3/+3de5hV1X3G8e/LdZDhIqIoF0EqoqMiUmIQihdQH2IaDPWCRoNGEmMarWlqUoWYtrahNjZaSNJEG1OJRihKiCQ1GoMkplHCRVQUEzEocjFyFQLIZeDXP/YaPAwDcwbOOTPMvJ/nmWf2XnudvX+zFuewztprrV01nSx3RBtkI816dW5LrzSaq7qeR9WcXtO19vcUxPIDdBKWt27BqV07AHDOSXuPHjutWwdGnLbvOa8d3GvPdkXX9tw7+oPh0DVNnfvr8/6MpWu3cEy71rRq0WyvNa02b6/kjxu38YPn32Lh2+/RubwV1w85gdO6tee0rh04onUL3lyzhU3bdnLOSUfv+Vsiggf+703+d9E7zH1zPW1aNWfCqNP5xIePZ+PWnbRq0YzdEbyxejNbtlfywtsb+Gi/rry3dQdHtGrByyveY+6b6+nVuS1vrd2SOgFjz7TNTm1b0altKzoe0ZLn/7COzu1ac8qx7Tm6XWvO7Xt0tkB++zJWbdzGjBdWsHz9+5S1bMapXTvw+Esruetnv9urDDq0ablnWmQVKRsY0qV9a97dtJ1mguf+sJbz+h7D1h2V/Or1Nby3dScndSnnlOPas31n1qFVnhbaf/3dbGpn5/LW/GW/49iyvZJtlbspb92CafOXs2t3MLRPZ/r36Mjr7/6JzdsrWbBsA9cO7sXtH2kYQ/ytNObOz96T/foV8KRbtsC998Ldd8PmzTBwIFx2GVT7YmJmZtUcwrSqYmjb9oO25pIlS5g4cSJz586lY8eOXHPNNWzbtm2f1+QuPN68eXMqK/ddmxWgdevWtebZnylTprB27VomT54MwKpVq1i6dCm9e/emWbNmVFZW0iJN3V6/fj2dO2fLfFRUVLBgwYJap9fVZaRTt27dWL58+Z79FStW0K1bt+ovpVu3bsyYMQOATZs2MX36dMrLy5kzZw7PPfcckyZNYvPmzezYsYO2bdvyta99rQ4lUhzudDIzs8NKTWs1SdozWqy68tYtOPGYcu685LT9nrOmUV6S+PTQ3nx6aO99juWuL1U1iu2D9cayhlXfY9tx+cAe1V/KP19y2p5RaFW27dxFM6nG9dS6dWzDTcP67JV2xYd6MPfN9eyo3E15WQsWr9rE/8x7m4+d0XXP2m1bd+xiy45dHF3eiqdfW83ogR24oKILdzz+Kr947V0EnN/3GI7vdAQvvL2Bealj7Zevr2bbzt307dKOiVf2p9dRbbnj8VeYvmAFbVu3oHJ3sHbzdq78UA+6dWzD1HnL+fWStfTo1IYjj2jFx/p15dIB3fdb1tY4BWLUKCjI2rAR8J3vwJ13wrvvwsc/DhMmQANZq8TMzA7epk2baNeuHe3bt+edd97hqaeeYsSIEQW9xpAhQ5g2bRpDhw5l0aJFLF68eJ88ixcvprKykpUrPxihPn78eKZOncq4ceM455xzeOSRRxgzZgxbt27l0UcfZeLEiQDcfvvt3HrrrfzkJz+hS5cubN++nYcffpixY8fudY26jHQaNGgQixcvZtmyZRx77LFMmzaNxx57bJ98VU+tk8SECRP2LF4+derUPXmq1p9qCB1O4E4nMzOzkqre4QT7jk7Lx1knfDC1qH+Pjnziw8fvN+8nz+61Z/vxzw854Hkjgl27Y684Z970F3u2d+0ONmzdQefy7M7iTcNOZHfU3BloTcvllxfoRBI8+ST07QszZsDZZxfoxGZmVt8GDBhARUUFJ598Mj179mTIkAO3Sw7GzTffzJgxY6ioqNjz06FDh73yTJkyhVGjRu2Vdumll3Lttdcybtw4vvnNb/LZz36We+65h927d3P99dczePBgAEaOHMmaNWsYNmwYkN2o/MxnPnNIMbds2ZJJkyZx4YUXsmvXLm644Qb69u0LZJ1hQ4YM4eKLL2bWrFnccccdAJx33nnceeedh3TdUvDT68zMzOyw4afXNTwDpVjGaiY9cjRXXXWQJ3n6afjqV+Ghh+DEE7PpdG3bghfMNzPLS0N6el19q6yspLKykrKyMpYsWcJFF13EkiVL9kyVs9r56XVmZmZm1mAEB9k5NH8+3HYbzJoFPXvCqlVZp1N5zdNlzczMarN582aGDx9OZWUlEcF9993nDqd65JI3MzMzs0NS506nCBgzJnsS3VFHZYve3ngjpAVhzczMDlbHjh1ZsGBBfYdhiTudzMzMzKw01q+HTp2yaXPdu8Mdd8Ctt0L79vUdmZmZmRVBIZ4xYmZmZmZNWK0jnTZuhPHjoUcPePbZLO1f/zV7Qp07nMzMCqIxrdds9afQ/47c6WRmZmZmh2S/nU7btsE3vgG9e8OECTByZNbxZGZmBVVWVsa6devc8WSHJCJYt24dZWVlBTtnUafXSRoBTASaA9+LiLuqHf8i8GmgElgDXB8Ry9KxXcCilPXtiBhZzFjNzMzM7ODU2OkUAYMHw8KFcNFF2cimAQNKH5yZWRPQvXt3VqxYwZo1a+o7FDvMlZWV0b1794Kdr2idTpKaA98GLgRWAPMkzYyIxTnZFgIDI2KrpM8BXwdGp2PvR0T/YsVnZmZm1tTkcUOwNfAD4M+BdcDoiHgr7wtEwC9+AcOGQfPm8OUvwzHHZPtmZlY0LVu25IQTTqjvMMz2UczpdWcBb0TE0ojYAUwFLsnNEBGzI2Jr2p0DFK47zczMzMz2yLkh+BGgArhKUkW1bGOBDRFxInAv8G/5nDsQ57f8Pxg6NBvVNH16duDKK93hZGZm1oQVs9OpG7A8Z39FStufscDPcvbLJM2XNEfSx4sRoJmZmVkTUusNwbQ/OW0/BgyXVMsq4fCjlldy7OVDYelSuO8+GDWqoIGbmZnZ4amoazrlS9I1wEDg3JzknhGxUlJv4BlJiyLiDzW89gbgBoDjjz++JPGamZmZHYZquiH44f3liYhKSRuBo4C1Bzrx2TyfLRR+yy1wxBEFDNnMzMwOZ8XsdFoJ5D6epHtK24ukC4DxwLkRsb0qPSJWpt9LJf0SOBPYp9MpIu4H7k/nWiNpWQH/hlydqaXBZQXl8i4tl3fpucxLy+VdesUq855FOKfVUe5NP2B7250bX2HcOBg3rj7Dsr35c6/hcZ00TK6Xhsd10jD1PZgXFbPTaR7QR9IJZJ1NVwKfyM0g6UzgPmBERKzOST8S2BoR2yV1BoaQLTJ+QBFxdAHj34uk+RExsFjnt725vEvL5V16LvPScnmXnsu8QcrnhmBVnhWSWgAdyBYU30u1m36u6wbI9dLwuE4aJtdLw+M6aZgkzT+Y1xVtTaeIqARuAp4CXgOmRcSrku6UNDJluxsoBx6V9KKkmSn9FGC+pJeA2cBd1Z56Z2ZmZmZ1s+eGoKRWZDcEZ1bLMxO4Nm1fBjwTEVHCGM3MzKwRKeqaThHxBPBEtbSv5mxfsJ/XPQecXszYzMzMzJqStEZT1Q3B5sD3q24IAvMjYibwAPCQpDeA9WQdU2ZmZmYHpUEsJH6YuL++A2hiXN6l5fIuPZd5abm8S89l3gDlcUNwG3B5HU/rum6YXC8Nj+ukYXK9NDyuk4bpoOpFHjFtZmZmZmZmZmaFVrQ1nczMzMzMzMzMrOlyp1MtJI2Q9HtJb0i6rb7jaSwkfV/Sakmv5KR1kvS0pCXp95EpXZImpTp4WdKA+ov88CSph6TZkhZLelXSLSndZV4EksokzZX0Uirvf0rpJ0j6bSrX/0kL+SKpddp/Ix3vVZ/xH64kNZe0UNJP077Lu4gkvSVpUXoQyPyU5s+URqy2NpHfW6WXR518Mf3f/7KkWZJ61kecTU2+3x8kXSopJPkpXUWWT51IuiKnrfxIqWNsivL4DDs+fYdZmD7HLq6POJuSmr6nVzte5zadO50OQFJz4NvAR4AK4CpJFfUbVaPxIDCiWtptwKyI6APMSvuQlX+f9HMD8J0SxdiYVAJ/FxEVwCDg8+nfssu8OLYDwyLiDKA/MELSIODfgHsj4kRgAzA25R8LbEjp96Z8Vne3kD0ttYrLu/jOj4j+OY819mdKI5Vnm8jvrRLKs04WAgMjoh/wGPD10kbZ9OT7/UFSO7L/t35b2gibnnzqRFIf4HZgSEScCnyh5IE2MXm+V74CTIuIM8kebPGfpY2ySXqQfb+n56pzm86dTgd2FvBGRCyNiB3AVOCSeo6pUYiIZ8meipPrEmBy2p4MfDwn/QeRmQN0lHRcaSJtHCLinYh4IW3/ieyLeTdc5kWRym1z2m2ZfgIYRtboh33Lu6oeHgOGS1KJwm0UJHUHPgp8L+0Ll3d98GdK45VPm8jvrdKqtU4iYnZEbE27c4DuJY6xKcr3+8M/k3XMbitlcE1UPnXyGeDbEbEBICJWlzjGpiifegmgfdruAKwqYXxN0n6+p+eqc5vOnU4H1g1YnrO/IqVZcXSJiHfS9h+BLmnb9VBAabrDmWR31lzmRZKmer0IrAaeBv4AvBcRlSlLbpnuKe90fCNwVGkjPuz9B/BlYHfaPwqXd7EF8HNJCyTdkNL8mdJ45VOHfm+VVl3fV2OBnxU1IoM86iVNR+kREf9bysCasHzeKycBJ0n6jaQ5kg400sMKI596+UfgGkkryJ68enNpQrMDqHObrkVRwzE7SBERkvxoxQKTVA5MB74QEZtyb0C7zAsrInYB/SV1BGYAJ9dzSI2WpL8EVkfEAknn1Xc8TchfRMRKSccAT0v6Xe5Bf6aYNRySrgEGAufWdyxNnaRmwD3AdfUciu2tBdl0ofPIRgQ+K+n0iHivXqOyq4AHI+Ibks4GHpJ0WkTsru2F1nB4pNOBrQR65Ox3T2lWHO9WDc1Lv6uGtboeCkBSS7IOpx9GxI9Sssu8yFJjZTZwNtnw06rO/twy3VPe6XgHYF2JQz2cDQFGSnqLbGj2MGAiLu+iioiV6fdqso7Vs/BnSmOWTx36vVVaeb2vJF0AjAdGRsT2EsXWlNVWL+2A04Bfpv+3BgEzvZh4UeXzXlkBzIyInRHxJvA6WSeUFU8+9TIWmAYQEc8DZUDnkkRn+1PnNp07nQ5sHtBH2ROQWpEtXjaznmNqzGYC16bta4HHc9LHpJXyBwEbc6ZvWB7SmhoPAK9FxD05h1zmRSDp6DTCCUltgAvJ1tGaDVyWslUv76p6uAx4JiI8QiRPEXF7RHSPiF5kn9PPRMTVuLyLRlLbtAguktoCFwGv4M+UxiyfNpHfW6VVa51IOhO4j6zDyWvUlMYB6yUiNkZE54jolf7fmkNWP/PrJ9wmIZ/Prx+TjXJCUmey6XZLSxlkE5RPvbwNDAeQdApZp9OakkZp1dW5TefpdQcQEZWSbgKeApoD34+IV+s5rEZB0hSyD/bOaY7uPwB3AdMkjQWWAVek7E8AFwNvAFuBT5U84MPfEOCTwKK0zhDAOFzmxXIcMDk9laMZ2VM3fippMTBV0r+QPVHogZT/AbLhwm+QLdx3ZX0E3Qj9PS7vYukCzEhTdFsAj0TEk5Lm4c+URml/bSJJdwLzI2Imfm+VVJ51cjdQDjya3q9vR8TIegu6CcizXqyE8qyTp4CLUlttF/CliPBIzSLKs17+DvgvSX9Ltpbkdb6ZUVz7+Z7eEiAivstBtOnkOjMzMzMzMzMzs0Lz9DozMzMzMzMzMys4dzqZmZmZmZmZmVnBudPJzMzMzMzMzMwKzp1OZmZmZmZmZmZWcO50MjMzMzMzMzOzgnOnk5nVC0m7JL2Y83NbAc/dS9IrhTqfmZmZWTHU0B7qdYC8BWnfSPqlpN9LeknSbyT1PYhz3ChpTNq+TlLXnGPfk1RR4DjnSeqfx2u+IOmIQ722mRVOi/oOwMyarPcjotbGg5mZmVkjVl/toasjYr6kG4C7gZF1eXFEfDdn9zrgFWBVOvbpQgXJB3F+iizOC2vJ/wXgYWBrAWMws0PgkU5m1qBIekvS1yUtkjRX0okpvZekZyS9LGmWpONTehdJM9JdsJckDU6nai7pvyS9Kunnktqk/H8jaXE6z9R6+jPNzMzMapTaPL+W9EL6GVxDnlNTO+nF1Kbpk9KvyUm/T1LzWi73LFDV1houaWFqg31fUuuUfldO2+nfU9o/SrpV0mXAQOCH6Zpt0gilgWk01N05MV8n6VsHGefzQLecc31H0vzUzvunlPY3QFdgtqTZKe0iSc+ncnxUUnkt1zGzAnOnk5nVlzbVhpOPzjm2MSJOB74F/EdK+yYwOSL6AT8EJqX0ScCvIuIMYADwakrvA3w7Ik4F3gMuTem3AWem89xYrD/OzMzMLA+57aEZKW01cGFEDABG80GbJ9eNwMQ0SmogsELSKSn/kJS+C7i6lut/DFgkqQx4EBid2mAtgM9JOgoYBZya2k7/kvviiHgMmE82Iql/RLyfc3h6em2V0cDUg4xzBPDjnP3xETEQ6AecK6lfREwiG211fkScL6kz8BXgglSW84Ev1nIdMyswT68zs/pyoOHkU3J+35u2zwb+Km0/BHw9bQ8DxgBExC5go6QjgTcj4sWUZwHQK22/THY37sfs3XgxMzMzK7Wa2kMtgW+lNYx2ASfV8LrngfGSugM/ioglkoYDfw7MkwTQhqwDqyY/lPQ+8BZwM9CXrO30ejo+Gfg82Q3AbcADkn4K/DTfPywi1khaKmkQsAQ4GfhNOm9d4mwFlAO55XRFmhrYAjgOqCBr4+UalNJ/k67TiqzczKyE3OlkZg1R7Ge7LrbnbO8ia9AAfBQ4h+zO3nhJp0dE5UFew8zMzKzQ/hZ4FziDbGbKtuoZIuIRSb8la9c8IemzgMhGhd+exzWujoj5VTuSOtWUKSIqJZ0FDAcuA24iu+GXr6nAFcDvgBkREcp6gPKOk+zm4d1ko97/StIJwK3AhyJig6QHgbIaXivg6Yi4qg7xmlmBeXqdmTVEo3N+V92Reg64Mm1fDfw6bc8CPgcgqbmkDvs7qaRmQI+ImA38PdCB7M6ZmZmZWUPRAXgnInYDnwT2We9IUm9gaZpS9jjZNLNZwGWSjkl5Oknqmec1fw/0qlpLM133V2kNpA4R8QRZZ9gZNbz2T0C7/Zx3BnAJcBVZBxR1jTMiArgDGCTpZKA9sIVsdHsX4CP7iWUOMCRnfdC2kmoaNWZmReSRTmZWX9pIejFn/8mIuC1tHynpZbLRSlV3p24G/lvSl4A1wKdS+i3A/ZLGko1o+hzwzn6u2Rx4OHVMCZgUEe8V7C8yMzMzO3T/CUyXNAZ4kqyDpborgE9K2gn8EZgQEeslfQX4ebrRtpNsKtuy2i4YEduUPSHuUUktgHnAd4FOwONpzSdR85pIDwLfTdP1zq523g2SXgMqImJuSltc1zgj4n1J3wC+FBFjJS0kGz21nGzKXpX7gSclrUrrOl0HTKlaFJ1sjafXMbOSUdZxbGbWMEh6CxgYEWvrOxYzMzMzMzM7eJ5eZ2ZmZmZmZmZmBeeRTmZmZmZmZmZmVnAe6WRmZmZmZmZmZgXnTiczMzMzMzMzMys4dzqZmZmZmZmZmVnBudPJzMzMzMzMzMwKzp1OZmZmZmZmZmZWcO50MjMzMzMzMzOzgvt/EgoucKjvwa0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Listing 5-12  - L1 Regularization\n",
    "#L1 Regularization    \n",
    "num_epochs = 500\n",
    "batch_size= 128\n",
    "\n",
    "weight_decay=0.0   #Set to 0; no L2 reg\n",
    "lambda_L1 = 0.0001 #Enables L1 Regularization\n",
    "\n",
    "model = NeuralNetwork()\n",
    "loss_function = nn.BCELoss()  #Binary Crosss Entropy Loss\n",
    "\n",
    "adam_optimizer = tch.optim.Adam(model.parameters(),lr= 0.001 ,weight_decay=weight_decay)\n",
    "\n",
    "#Define hyperparater for L1 Regularization\n",
    "#Train network\n",
    "adam_loss = train_network(model,adam_optimizer,loss_function ,num_epochs,batch_size\n",
    ",X_train,Y_train,lambda_L1=lambda_L1)\n",
    "\n",
    "#Evaluate model\n",
    "evaluate_model(model,x_test,y_test,X_train,Y_train,adam_loss)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Listing 5-13  - L2 Regularization\n",
    "\n",
    "#L2 Regularization    \n",
    "num_epochs = 500\n",
    "batch_size= 128\n",
    "weight_decay=0.001 #Enables L2 Regularization\t\n",
    "lambda_L1 = 0.00    #Set to 0; no L1 reg\n",
    "\n",
    "model = NeuralNetwork()\n",
    "loss_function = nn.BCELoss()  #Binary Crosss Entropy Loss\n",
    "\n",
    "adam_optimizer = tch.optim.Adam(model.parameters(),lr= 0.001,weight_decay=weight_decay)\n",
    "\n",
    "#Train Network\n",
    "adam_loss = train_network(model,adam_optimizer,loss_function,num_epochs,batch_size\n",
    ",X_train,Y_train,lambda_L1=lambda_L1)\n",
    "\n",
    "#Evaluate model\n",
    "evaluate_model(model,x_test,y_test,X_train,Y_train,adam_loss)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 - Loss:1.6601\n",
      "\n",
      " Model Performance -\n",
      "Training Accuracy- 0.872\n",
      "Training Precision- 0.861\n",
      "Training Recall- 0.871\n",
      "Training ROCAUC 0.945\n",
      "Validation Accuracy- 0.849\n",
      "Validation Precision- 0.839\n",
      "Validation Recall- 0.843\n",
      "Validation ROCAUC 0.919\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Listing 5-14  - Dropout Regularization\n",
    "\n",
    "\n",
    "#Define Network with Dropout Layers\n",
    "class NeuralNetwork(nn.Module):\n",
    "    #Adding droput layers within Neural Network to reduce overfitting\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        tch.manual_seed(2020)\n",
    "        self.fc1 = nn.Linear(48, 96)\n",
    "        self.fc2 = nn.Linear(96, 192)\n",
    "        self.fc3 = nn.Linear(192, 384)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.out = nn.Linear(384, 1)\n",
    "        self.final = nn.Sigmoid()\n",
    "        self.drop = nn.Dropout(0.1)  #Dropout Layer\n",
    "\n",
    "\n",
    "        \n",
    "    def forward(self, x):\n",
    "        op = self.drop(x)  #Dropout for input layer\n",
    "        op = self.fc1(op)\n",
    "        op = self.relu(op)        \n",
    "        op = self.drop(op) #Dropout for hidden layer 1\n",
    "        op = self.fc2(op)\n",
    "        op = self.relu(op)\n",
    "        op = self.drop(op) #Dropout for hidden layer 2\n",
    "        op = self.fc3(op)\n",
    "        op = self.relu(op)      \n",
    "        op = self.drop(op) #Dropout for hidden layer 3       \n",
    "        op = self.out(op)\n",
    "        y = self.final(op)\n",
    "        return y\n",
    "    \n",
    "num_epochs = 500\n",
    "batch_size= 128\n",
    "\n",
    "weight_decay=0.0 #Set to 0; no L2 reg\n",
    "lambda_L1 = 0.0  #Set to 0; no L1 reg\n",
    "\n",
    "\n",
    "model = NeuralNetwork()\n",
    "loss_function = nn.BCELoss()  #Binary Crosss Entropy Loss\n",
    "\n",
    "adam_optimizer = tch.optim.Adam(model.parameters(),lr= 0.001\n",
    ",weight_decay=weight_decay)\n",
    "#Train model\n",
    "adam_loss = train_network(model,adam_optimizer,loss_function,num_epochs\n",
    ",batch_size,X_train,Y_train\n",
    ",lambda_L1= lambda_L1)\n",
    "\n",
    "#Evaluate model\n",
    "evaluate_model(model,x_test,y_test,X_train,Y_train,adam_loss)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 - Loss:1.9866\n",
      "\n",
      " Model Performance -\n",
      "Training Accuracy- 0.821\n",
      "Training Precision- 0.83\n",
      "Training Recall- 0.782\n",
      "Training ROCAUC 0.903\n",
      "Validation Accuracy- 0.81\n",
      "Validation Precision- 0.821\n",
      "Validation Recall- 0.765\n",
      "Validation ROCAUC 0.901\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Listing 5-15  - L1, L2 + Dropout Regularization\n",
    "\n",
    "\n",
    "#Create a network with Dropout layer\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        tch.manual_seed(2020)\n",
    "        self.fc1 = nn.Linear(48, 96)\n",
    "        self.fc2 = nn.Linear(96, 192)\n",
    "        self.fc3 = nn.Linear(192, 384)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.out = nn.Linear(384, 1)\n",
    "        self.final = nn.Sigmoid()\n",
    "        self.drop = nn.Dropout(0.1)  #Dropout Layer\n",
    "\n",
    "\n",
    "        \n",
    "    def forward(self, x):\n",
    "        op = self.drop(x)  #Dropout for input layer\n",
    "        op = self.fc1(op)\n",
    "        op = self.relu(op)        \n",
    "        op = self.drop(op) #Dropout for hidden layer 1\n",
    "        op = self.fc2(op)\n",
    "        op = self.relu(op)\n",
    "        op = self.drop(op) #Dropout for hidden layer 2\n",
    "        op = self.fc3(op)\n",
    "        op = self.relu(op)      \n",
    "        op = self.drop(op) #Dropout for hidden layer 3       \n",
    "        op = self.out(op)\n",
    "        y = self.final(op)\n",
    "        return y\n",
    "    \n",
    "num_epochs = 500\n",
    "batch_size= 128\n",
    "\n",
    "lambda_L1    = 0.0001  #Enabled L1 \n",
    "weight_decay =0.001    #Enabled L2\n",
    "\n",
    "model = NeuralNetwork()\n",
    "loss_function = nn.BCELoss()\n",
    "\n",
    "adam_optimizer = tch.optim.Adam(model.parameters(),lr= 0.001 ,weight_decay=weight_decay)\n",
    "\n",
    "adam_loss = train_network(model,adam_optimizer,loss_function ,num_epochs,batch_size\n",
    ",X_train,Y_train,lambda_L1=lambda_L1)\n",
    "\n",
    "evaluate_model(model,x_test,y_test,X_train,Y_train,adam_loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
